AIRWiki - User contributions [en]

User:RossellaBlatt

2009-05-20T15:12:49Z

RossellaBlatt:

{{SMWUser
|category=PhD
|firstname=Rossella
|lastname=Blatt
|email=blatt@elet.polimi.it
|advisor=MatteoMatteucci
|resarea = BioSignal Analysis
|projectpage = BCI based on Motor Imagery; Lung Cancer Detection by an Electronic Nose
|photo=FriendshipGraduating.jpg
}}

== Personal info ==

I am a PhD student in Computer Science at the Politecnico di Milano, Italy, since January 2007 in the Artificial Intelligence field (PhD Cycle: XXII).

My main research areas regard signal classification and recognition, with particular emphasis to the [[Lung_Cancer_Detection_by_an_Electronic_Nose|Olfactory Signal Processing]] and [[BCI_based_on_Motor_Imagery|Brain Computer Interfaces]] fields.

In October 2006 I received a Msc in Telecommunications Engineering - Signal Processing from the Politecnico di Milano. The master thesis proposed a tool to diagnose lung cancer by the analysis of the olfactory signal acquired through an electronic nose.
I received a Bsc from the Politecnico di Milano in Telecommunications Engineering, in March 2004, with a thesis titled “Genomic Signal Processing: identification of exons in DNA sequences by an analysis in the frequency domain”.

== Links ==
My Web Page in the department website: http://www.elet.polimi.it/people/blatt

My personal Web Page: http://www.freewebs.com/rossellablatt

If you want to contact me, my email address is: [mailto:blatt@elet.polimi.it blatt@elet.polimi.it]

IIT-Lab

2009-04-01T15:21:39Z

RossellaBlatt:

== What is the IIT-Lab ==

AIRLab-IITLab is dedicated to activities founded by the Italian Institute of Technology.
The lab hosts activities related to Brain-Computer Interfaces (BCI) and Affective Computing.

=== Location ===
It is located in the Rimembranze di Lambrate building of the Department of Electronics and Information, Via Rimembranze di Lambrate, 14, Milan.

=== Access Rules ===
The access to AIRLab-IITLab is reserved to registered users. If you are student and want to register, you have to fill the AIRLab registration form (to be signed by your tutor) and the security form. The key of the lab is provided to registered users by the doorkeeper at the main entrance of the Lambrate building.

=== Booking ===

Please book the instrument you want to use by adding an entry to the table; the booking of an instrument implies the booking of the room. If you want to use a different instrument at the same time of an existing booking, please contact the other person involved and check that you can share the room; alternatively, you can ask the doorkeeper for an empty room in the building.


{| border="1"
! Day !! Time !! Person !! Instrument
|-
| Every wednesday || 9:00-19:30 || [[User:RossellaBlatt]] || EEG
|-
| Every thursday (except April 2nd) || 9:00-19:30 || [[User:RossellaBlatt]] || EEG
|-
| Thursday 2 April || 9:30-14:00 || [[User:BernardoDalSeno]] || EEG
|-
| Friday 3 April || 14:00-17:00 || [[User:BernardoDalSeno]] || EEG
|-
|}

== Links ==

* [[Brain-Computer Interface]] page on this Wiki
* [[Affective Computing]] page on this Wiki
* [http://www.airlab.elet.polimi.it/index.php/airlab/visitor_info/airlab_iitlab AIRLab - IITLab]

Master Level Theses

2009-02-26T12:00:46Z

RossellaBlatt: /* Analysis of the Olfactory Signal */

Here you can find proposals for master thesis (20 CFU for each student)


==== BioSignal Analysis ====

===== Analysis of the Olfactory Signal =====
{{Project template
|title=Computational Intelligence techniques to analyse the olfactory signal acquired by an electronic nose for cancer diagnosis
|tutor=[[User:AndreaBonarini|Andrea Bonarini]] ([mailto:bonarini@elet.polimi.it email]), [[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucci@elet.polimi.it email]), [[User:RossellaBlatt|Rossella Blatt]] ([mailto:blatt@elet.polimi.it email])
|description= The electronic nose is an instrument able to detect and recognize odors, that is the volatile substances in the atmosphere or emitted by the analyzed substance. This device can react to a gas substance by providing signals that can be analyzed to classify the input. It is composed of a sensor array (MOS sensors, in our case) and a pattern classification system based on machine learning techniques. Each sensor reacts in a different way to the analyzed substance, providing multidimensional data that can be considered as a unique olfactory blueprint of the analyzed substance. We have already tested the use of the electronic nose as diagnostic tool for lung cancer; boosted from the very satisfactory results that we have achieved by these analysis, we want to investigate the possibility of diagnosing other types of cancer and to improve the current computation intelligence techniques.
The project is done in collaboration with the Istituto dei Tumori, Milano.

;Tools and instruments: Matlab

;Bibliography : BLATT R., BONARINI A, CALABRÒ E, DELLA TORRE M, MATTEUCCI M, PASTORINO U. (2008). Pattern Classification Techniques for Early Lung Cancer Diagnosis using an Electronic Nose. In: Frontiers in Artificial Intelligence and Applications. European Conference on Artificial Intelligence - Prestigious Applications of Intetelligent Systems. Patras, Greece. 21-15 luglio 2008. (vol. 178, pp. 693-697). ISBN/ISSN: 978-1-58603-891-5. IOS Press. [[Image:PAIS.pdf|Paper-PAIS2008]]

|start=Anytime (a new acquisition phase will start in March)
|number=1-2
|cfu=20
|image=Acquisition.jpg}}

===== Sleep Staging =====
{{Project template
|title=Development of a computer-assisted CAP (Sleep cyclic alternating pattern) scoring method
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), Martin Mendez ([mailto:martin.mendez@polimi.it email]), Anna Maria Bianchi ([mailto:annamaria.bianchi@polimi.it email]), Mario Terzano (Ospedale di Parma)
|description=In 1985, Terzano describes for the first time the Cyclic Alternating Pattern [http://en.wikipedia.org/wiki/Cyclical_alternating_pattern] during sleep and, nowadays, CAP is widely accepted by the medical community as basic analysis of sleep. The CAP evaluation is of fundamental importance since it represents the mechanism developed by the brain evolution to monitor the inner and outer world and to assure the survival during sleep. However, visual detection of CAP in polisomnography (i.e., the standard procedure) is a slow and time-consuming process. This limiting factor generates the necessity of new computer-assisted scoring methods for fast CAP evaluation. This thesis deals with the development of a Decision Support System for CAP scoring based on features extraction at multi-system level (by statistical and signal analysis) and Pattern Recognition or Machine Learning approaches. This may allow the automatic detection of CAP sleep and could be integrated, through reinforcement learning techniques, with the corrections given by physicians.

;Tools and instruments
:Matlab, C/C++

;Bibliography
: Mario Terzano, Liborio Parrino. ''Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (CAP) in human sleep'', Sleep Medicine 2 (2001) 537–553. [http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6W6N-44DY2B4-8&_user=2620285&_coverDate=11%2F30%2F2001&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000058180&_version=1&_urlVersion=0&_userid=2620285&md5=aa61a060d005f23f6afed5c1fc2f1126]
|start=Anytime
|number=1-2
|cfu=20
|image=CAP_Sleep_Staging.jpg}}

===== Brain-Computer Interface =====
{{Project template
|title=Recognition of the user's focusing on the stimulation matrix
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=A [http://en.wikipedia.org/wiki/P300_(Neuroscience) P300]-based [[Brain-Computer_Interface|BCI]] stimulates the user continuously, and the detection of a P300 designates the choice of the user. When the user is not paying attention to the interface, false positives are likely. The objective of this work is to avoid this problem; the analysis of the electroencephalogram (EEG) over the visual cortex (and possibly an analysis of P300s or of other biosignals) should tell when the user is looking at the interface.

;Tools and instruments
:Matlab, [http://www.bci2000.org/ BCI2000], C++
:EEG system

;Bibliography
: E. Donchin, K.M. Spencer, R. Wijesinghe. ''The Mental Prosthesis: Assessing the Speed of a P300-Based Brain-Computer Interface'' [http://www.cs.cmu.edu/~tanja/BCI/P300Speed_2000.pdf]
|start=Anytime
|number=1-2
|cfu=20
|image=B_p300_speller.jpg}}

{{Project template
|title=Creation of new EEG training by introduction of noise
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=A [[Brain-Computer Interface|BCI]] must be trained on the individual user in order to be effective. This training phase require recording data in long sessions, which is time consuming and boring for the user. The aim of this project is to develop algorithm to create new training EEG (electroencephalography) data from existing ones, so as to speed up the training phase.

;Tools and instruments
:Matlab, [http://www.bci2000.org/ BCI2000]
:Knowledge of C++ may be useful
:EEG system

;Bibliography
: J.R. Wolpaw et al. ''Brain-computer interfaces for communication and control'' [http://scienceserver.cilea.it/cgi-bin/sciserv.pl?collection=journals&journal=13882457&issue=v113i0006&article=767_bifcac&form=pdf&file=file.pdf]
|start=Anytime
|number=1
|cfu=20
|image=Bci_arch.png}}

{{Project template
|title=Real-time removal of ocular artifact from EEG
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=In a [[Brain-Computer Interface|BCI]] based on electroencephalogram (EEG), one of the most important sources of noise is related to ocular movements. Algorithms have been devised to cancel the effect of such artifacts. The project consists in the in the implementation in real time of an existing algorithm (or one newly developed) in order to improve the performance of a BCI.

;Tools and instruments
:Matlab, [http://www.bci2000.org/ BCI2000], C++
:EEG-system

;Bibliography
: J.R. Wolpaw et al. ''Brain-computer interfaces for communication and control'' [http://scienceserver.cilea.it/cgi-bin/sciserv.pl?collection=journals&journal=13882457&issue=v113i0006&article=767_bifcac&form=pdf&file=file.pdf]
: R.J. Croff, R.J. Barry. ''Removal of ocular artifact from the EEG: a review'' [http://scienceserver.cilea.it/cgi-bin/sciserv.pl?collection=journals&issn=09877053&volume=30&issue=1&firstpage=5&form=html]
|number=1
|cfu=10-20
|image=B_bci.jpg}}

{{Project template
|title=Aperiodic visual stimulation in a VEP-based BCI
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=[http://en.wikipedia.org/wiki/Evoked_potential#Visual_evoked_potential Visual-evoked potentials] (VEPs) are a possible way to drive the a [[Brain-Computer Interface|BCI]]. This projects aims at maximizing the discrimination between different stimuli.

;Tools and instruments
:Matlab, [http://www.bci2000.org/ BCI2000], C++
:EEG system

;Bibliography
: J.R. Wolpaw et al. ''Brain-computer interfaces for communication and control'' [http://scienceserver.cilea.it/cgi-bin/sciserv.pl?collection=journals&journal=13882457&issue=v113i0006&article=767_bifcac&form=pdf&file=file.pdf]

|number=1
|cfu=20
|image=Bci_arch.png}}

{{Project template
|title=Driving an autonomous wheelchair with a P300-based BCI
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=This project pulls together different Airlab projects with the aim to drive an autonomous wheelchair ([[LURCH - The autonomous wheelchair|LURCH]]) with a [[Brain-Computer Interface|BCI]], through the development of key software modules. The work will be validated with live experiments.

;Tools and instruments
:C++, C, [http://www.bci2000.org/ BCI2000], Matlab
:Linux
:EEG system
:Lurch wheelchair

;Bibliography
: R. Blatt et al. ''Brain Control of a Smart Wheelchair'' [http://www.booksonline.iospress.com/Content/View.aspx?piid=9401]
|start=November 2008
|number=1
|cfu=5-20
|image=LURCH_wheelchair.jpg}}

{{Project template
|title=Online automatic tuning of the number of repetitions in a P300-based BCI
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:BernardoDalSeno|Bernardo Dal Seno]] ([mailto:dalseno%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description=In a [http://en.wikipedia.org/wiki/P300_(Neuroscience) P300]-based [[Brain-Computer_Interface|BCI]], (visual) stimuli are presented to the user, and the intention of the user is recognized when a P300 potential is recognized in response of the desired stimulus. In order to improve accuracy, many stimulation rounds are usually performed before making a decision. The exact number of repetitions depends on the user and the goodness of the classifier, but it is usually fixed a-priori. The aim of this project is to adapt the number of repetitions to changing conditions, so as to achieve the maximum accuracy with the minimum time.
The work will be validated with live experiments.

;Tools and instruments
:C++, [http://www.bci2000.org/ BCI2000], Matlab
:EEG system

;Bibliography
: E. Donchin, K.M. Spencer, R. Wijesinghe. ''The Mental Prosthesis: Assessing the Speed of a P300-Based Brain-Computer Interface'' [http://www.cs.cmu.edu/~tanja/BCI/P300Speed_2000.pdf]
|start=Anytime
|number=1
|cfu=5-20
|image=B_p300_speller.jpg}}





==== Machine Learning ====

{{Project template
|title=Reinforcement Learning in Poker
|tutor=Marcello Restelli (restelli-AT-elet-DOT-polimi-DOT-it)
|description=In this years, Artificial Intelligence research has shifted its attention from fully observable environments such as Chess to more challenging partially observable ones such as Poker.

Up to this moment research in this kind of environments, which can be formalized as Partially Observable Stochastic Games, has been more from a game theoretic point of view, thus focusing on the pursue of optimality and equilibrium, with no attention to payoff maximization, which may be more interesting in many real-world contexts.

On the other hand Reinforcement Learning techniques demonstrated to be successful in solving both fully observable problems, single and multi-agent, and single-agent partially observable ones, while lacking application to the partially observable multi-agent framework.

This research aims at studying the solution of Partially Observable Stochastic Games, analyzing the possibility to combine the Opponent Modeling concept with the well proven Reinforcement Learning solution techniques to solve problems in this framework, adopting Poker as testbed.
|start=Anytime
|number=1-2
|cfu=20-40
|image=PokerPRLT.png}}

{{Project template
|title= EyeBot
|tutor= Daniele Loiacono (loiacono-AT-elet-DOT-polimi-DOT-it), Alessandro Giusti (giusti-AT-elet-DOT-polimi-DOT-it), and Pierluigi Taddei (taddei-AT-elet-DOT-polimi-DOT-it)
|description=[http://torcs.sourceforge.net/ TORCS] is a state-of-the-art open source racing simulator that represents an ideal bechmark for machine learning techniques. We already organized two successfull competitions based on TORCS where competitors have been asked to develop a controller using their preferred machine learning techniques. So far, the controller developed for TORCS used as input only information extracted directly from the state of the game. The goal of this project is to extend the existing controller API (see [http://cig.dei.polimi.it/ here]) to use the visual information (e.g. the screenshots of the game) as input to the controllers. A successfull project will include both the development of the API and some basic imaga preprocessing to extract information from the images.
|start=Anytime
|number=1 to 2
|cfu=20
|image=TORCS2.jpg}}

{{Project template
|title= SmarTrack
|tutor= Daniele Loiacono (loiacono-AT-elet-DOT-polimi-DOT-it)
|description=The generation of customized game content for each player is an attractive direction to improve the game experience in the next-generation computer games. In this scenario, Machine Learning could play an important role to provide automatically such customized game content.
The goal of this project is to apply machine learning techniques for the generation of customized tracks in
[http://torcs.sourceforge.net/ TORCS], a state-of-the-art open source racing simulator. The project include different activities: the automatic generation of tracks, the section of relevant features to characterize a track and the analysis of an interest measure.
|start=Anytime
|number=1 to 2
|cfu=20
|image=TORCS3.jpg}}

{{Project template
|title=Automatic generation of domain ontologies
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:AndreaBonarini|Andrea Bonarini]] ([mailto:bonarini%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description= This thesis to be developed together with [http://www.noustat.it/ Noustat S.r.l.], who are developing research activities directed toward the optimization of knowledge management services, in collaboration with another company operating in this field. This project is aimed at removing the ontology building bottleneck, long and expensive activity that usually requires the direct collaboration of a domain expert. The possibility of automatic building the ontology, starting from a set of textual documents related to a specific domain, is expected to improve the ability to provide the knowledge management service, both by reducing the time-to-application, and by increasing the number of domains that can be covered. For this project, unsupervised learning methods will be applied in sequence, exploiting the topological properties of the ultra-metric spaces that emerge from the taxonomic structure of the concepts present in the texts, and associative methods will extend the concept network to lateral, non-hierarchical relationships.

|start=before November 30th
|number=1-2
|cfu=20
|image=}}

==== Affective Computing ====

==== Ontologies and Semantic Web ====

{{Project template
|title=Automatic generation of domain ontologies
|tutor=[[User:MatteoMatteucci|Matteo Matteucci]] ([mailto:matteucc%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email]), [[User:AndreaBonarini|Andrea Bonarini]] ([mailto:bonarini%40%65%6c%65%74%2e%70%6f%6c%69%6d%69%2e%69%74 email])
|description= This thesis to be developed together with [http://www.noustat.it/ Noustat S.r.l.], who are developing research activities directed toward the optimization of knowledge management services, in collaboration with another company operating in this field. This project is aimed at removing the ontology building bottleneck, long and expensive activity that usually requires the direct collaboration of a domain expert. The possibility of automatic building the ontology, starting from a set of textual documents related to a specific domain, is expected to improve the ability to provide the knowledge management service, both by reducing the time-to-application, and by increasing the number of domains that can be covered. For this project, unsupervised learning methods will be applied in sequence, exploiting the topological properties of the ultra-metric spaces that emerge from the taxonomic structure of the concepts present in the texts, and associative methods will extend the concept network to lateral, non-hierarchical relationships.

|start=before November 30th
|number=1-2
|cfu=20
|image=OntologyFromText.jpg}}



==== Robotics ====
{{Project template
|title= Robot games
|tutor= Andrea Bonarini (bonarini-AT-elet-DOT-polimi-DOT-it)
|description=The goal of this activity is to develop an interactive game with robots using commercial devices such as the WII Mote (see the [http://airwiki.elet.polimi.it/mediawiki/index.php/Robogames Robogames page])
Projects are available in different areas:
* Design and implementation of the game on one of the available robots and extension of the robot functionalities
* Design and implementation of the game and a new suitable robot
* Evaluation of the game with users (in collaboration with [http://www.elet.polimi.it/people/garzotto Franca Garzotto])

These projects allow to experiment with real mobile robots and real interaction devices.

Parts of these projects can be considered as course projects. These projects can also be extended to cover course projects.
|start=Anytime
|number=1-2
|cfu=7.5-20
|image=Robowii_robot.jpg}}

File:Acquisition.JPG

2009-02-26T11:59:07Z

RossellaBlatt: uploaded a new version of "Image:Acquisition.JPG"

File:Acquisition.JPG

2009-02-26T11:03:37Z

RossellaBlatt:

RossellaBlatt: /* Description and results of experiments */

Lung Cancer Detection by an Electronic Nose

2009-02-10T16:23:01Z

RossellaBlatt: /* Link to source code of the software written for the project */

Lung Cancer Detection by an Electronic Nose

2009-02-10T16:22:49Z

RossellaBlatt: /* Photos and videos */

Lung Cancer Detection by an Electronic Nose

2009-02-10T16:22:39Z

RossellaBlatt: /* Description and results of experiments */

2008-07-14T13:12:46Z

RossellaBlatt: /* Booking */

== BeLight ==

A brief description of EbNeuro BeLight should go here.

=== Booking ===



{| border="1"
! Day !! Time !! Person
|-
| Thursday 10 July || 10:00 - 19:00 || Bernardo
|-
| Friday 11 July || 15:00 - 19:00 || Rossella
|-
| Tuesday 15 July || 9:00 - 19:00 || Rossella
|-
| Thursday 17 July || 10:30 - 19:00 || Rossella
|-
| Friday 18 July || 10:30 - 19:00 || Rossella
|-
| Thursday 31 July || 10:30 - 19:00 || Rossella
|-
| Friday 1 August || 10:30 - 19:00 || Rossella
|-
|}

File:PAIS.pdf

2008-06-17T12:04:13Z

RossellaBlatt:

Lung Cancer Detection by an Electronic Nose

2008-06-17T12:03:49Z

RossellaBlatt: /* Link to project documents and files */

== '''Part 1: project profile''' ==

=== Project name ===

Lung Cancer Detection by an Electronic Nose

=== Project short description ===

The electronic nose is an instrument able to detect and recognize odors, that is the volatile substances in the atmosphere or emitted by the analyzed substance. This device can react to a gas substance by providing signals that can be analyzed to classify the input. It is composed of a sensor array (6 MOS sensors, in our case) and a pattern classification process based on machine learning techniques.
In this project, we have been using an electronic nose based on an array of six MOS sensors, to recognize the presence of lung cancer in breaths' subjects, diagnosing the disease with a non invasive and low cost method.

During the first phase of our research, we have evaluated the possibility and accuracy of lung cancer diagnosis by classifying the
olfactory signal associated to exhalations of subjects.

At the end of the first phase, results have been very satisfactory and promising: we achieved an average accuracy of 92.6%, sensitivity of
95.3% and specificity of 90.5%. In particular we analyzed the breath of 101 individuals, of which 58 control subjects, and 43 suffer from
different types of lung cancer (primary and not) at different stages.
In order to find the components able to discriminate between the two classes ‘healthy’ and ‘sick’ at best, and to reduce the dimensionality
of the problem, we have extracted the most significant features and projected them into a lower dimensional space using Non Parametric
Linear Discriminant Analysis. Finally, we have used these features as input to several supervised pattern classification algorithms, based
on different k-nearest neighbors (k-NN) approaches (classic, modified and Fuzzy k-NN), linear and quadratic discriminant classifiers
and on a feed-forward artificial neural network (ANN). The observed results have all been validated using cross-validation.

These results pushed us to begin the second phase of the project, still in progress, to investigate the possibility of early lung cancer diagnosis: we are involving a larger number of subjects, partioned in different classes according to the type and stage of the disease. The research demonstrates that the electronic nose is a promising alternative to current lung cancer diagnostic techniques: the obtained predictive errors are lower than those achieved by present diagnostic methods, and the cost of the analysis, both in money, time and resources, is lower. The introduction of this technology will lead to very important social and business effects: its low price and small dimensions allow a large scale distribution, giving the opportunity to perform non invasive, cheap, quick, and massive early diagnosis and screening.

=== Dates ===
Start date: 2007/01/01

End date: --

=== Website(s) ===

At the moment no website avaible

=== People involved ===

===== Project head(s) =====

A. Bonarini - [[User:AndreaBonarini]]

M. Matteucci - [[User:MatteoMatteucci]]

===== Other Politecnico di Milano people =====

R. Blatt - [[User:RossellaBlatt]]

===== Students currently working on the project =====

Claudio Trameri - [[User:ClaudioTrameri]]

Mauro Verdirosa - [[User:MauroVerdirosa]]

===== Students who worked on the project in the past =====

===== External personnel: =====

Dott. Ugo Pastorino (Istituto dei Tumori - Milano)

Dott. Elisa Calabrò (Istituto dei Tumori - Milano)

Dott. Matteo Della Torre (SACMI - Imola)

=== Laboratory work and risk analysis ===

Laboratory work for this project will be mainly performed at the Istituto Nazionale dei Tumori di Milano, where the acquisistion of subjects' breath, both sick and healthy will be done.
For this kind of work, there are not potential risks.

== '''Part 2: project description''' ==

=== State of the art ===

=== Preliminary and sketches ===

=== Design notes and guidelines ===

=== Link to project documents and files ===

Results obtained from this work have been presented at different conferences:

* '''Prestigious Applications of Intelligent Systems (PAIS 2008), Patras, Greece'''
:The 5th Prestigious Applications of Intelligent Systems (PAIS 2008) is a sub-conference of the 18th European Conference on Artificial Intteligence (ECAI 2008) that will be held at the University of Patras, Greece, from July 21st to 25th.
:[[Image:PAIS.pdf|Paper-PAIS2008]]

* '''International Joint Conference on Neural Networks (IJCNN 2007), Orlando, FL, USA'''
:'''Lung Cancer Identification by an Electronic Nose based on array of MOS Sensors''', Blatt Rossella, Bonarini Andrea, Calabrò Elisa, Della Torre Matteo, Matteucci Matteo, Pastorino Ugo. Proceedings of the 2007 International Joint Conference on Neural Networks (IJCNN 2007), Orlando, FL, USA: [[Image:IJCNNfinal.pdf|Paper-IJCNN2007]]

:Short presentation of the ''Lung Cancer Identification by an Electronic Nose based on an array of MOS Sensors'' paper: [[Image:LungCancerIdentificationIJCNN2007.pdf|Presentation-IJCNN2007]]

* '''International Workshop on Fuzzy Logic and Applications (WILF 2007), Ruta di Camogli, Genova, Italy'''

: '''Fuzzy k-NN Lung Cancer Identification by an Electronic Nose''', Blatt Rossella, Bonarini Andrea, Calabrò Elisa, Della Torre Matteo, Matteucci Matteo, Pastorino Ugo. Proceedings of the 7th International Workshop on Fuzzy Logic and Applications, WILF 2007, Lecture Notes in Computer Science (LNAI), LNAI 4578, pages 261-268, Springer. Camogli (GE), Italy, July 2007.

=== Description and results of experiments ===

=== Photos and videos ===

=== Link to source code of the software written for the project ===

=== Description and results of experiments ===

=== Useful internet links ===

Lung Cancer Detection by an Electronic Nose

2008-06-17T12:02:34Z

RossellaBlatt: /* Link to project documents and files */

== '''Part 1: project profile''' ==

=== Project name ===

Lung Cancer Detection by an Electronic Nose

=== Project short description ===

The electronic nose is an instrument able to detect and recognize odors, that is the volatile substances in the atmosphere or emitted by the analyzed substance. This device can react to a gas substance by providing signals that can be analyzed to classify the input. It is composed of a sensor array (6 MOS sensors, in our case) and a pattern classification process based on machine learning techniques.
In this project, we have been using an electronic nose based on an array of six MOS sensors, to recognize the presence of lung cancer in breaths' subjects, diagnosing the disease with a non invasive and low cost method.

During the first phase of our research, we have evaluated the possibility and accuracy of lung cancer diagnosis by classifying the
olfactory signal associated to exhalations of subjects.

At the end of the first phase, results have been very satisfactory and promising: we achieved an average accuracy of 92.6%, sensitivity of
95.3% and specificity of 90.5%. In particular we analyzed the breath of 101 individuals, of which 58 control subjects, and 43 suffer from
different types of lung cancer (primary and not) at different stages.
In order to find the components able to discriminate between the two classes ‘healthy’ and ‘sick’ at best, and to reduce the dimensionality
of the problem, we have extracted the most significant features and projected them into a lower dimensional space using Non Parametric
Linear Discriminant Analysis. Finally, we have used these features as input to several supervised pattern classification algorithms, based
on different k-nearest neighbors (k-NN) approaches (classic, modified and Fuzzy k-NN), linear and quadratic discriminant classifiers
and on a feed-forward artificial neural network (ANN). The observed results have all been validated using cross-validation.

These results pushed us to begin the second phase of the project, still in progress, to investigate the possibility of early lung cancer diagnosis: we are involving a larger number of subjects, partioned in different classes according to the type and stage of the disease. The research demonstrates that the electronic nose is a promising alternative to current lung cancer diagnostic techniques: the obtained predictive errors are lower than those achieved by present diagnostic methods, and the cost of the analysis, both in money, time and resources, is lower. The introduction of this technology will lead to very important social and business effects: its low price and small dimensions allow a large scale distribution, giving the opportunity to perform non invasive, cheap, quick, and massive early diagnosis and screening.

=== Dates ===
Start date: 2007/01/01

End date: --

=== Website(s) ===

At the moment no website avaible

=== People involved ===

===== Project head(s) =====

A. Bonarini - [[User:AndreaBonarini]]

M. Matteucci - [[User:MatteoMatteucci]]

===== Other Politecnico di Milano people =====

R. Blatt - [[User:RossellaBlatt]]

===== Students currently working on the project =====

Claudio Trameri - [[User:ClaudioTrameri]]

Mauro Verdirosa - [[User:MauroVerdirosa]]

===== Students who worked on the project in the past =====

===== External personnel: =====

Dott. Ugo Pastorino (Istituto dei Tumori - Milano)

Dott. Elisa Calabrò (Istituto dei Tumori - Milano)

Dott. Matteo Della Torre (SACMI - Imola)

=== Laboratory work and risk analysis ===

Laboratory work for this project will be mainly performed at the Istituto Nazionale dei Tumori di Milano, where the acquisistion of subjects' breath, both sick and healthy will be done.
For this kind of work, there are not potential risks.

== '''Part 2: project description''' ==

=== State of the art ===

=== Preliminary and sketches ===

=== Design notes and guidelines ===

=== Link to project documents and files ===

Results obtained from this work have been presented at different conferences:

* '''Prestigious Applications of Intelligent Systems (PAIS 2008), Patras, Greece'''
:The 5th Prestigious Applications of Intelligent Systems (PAIS 2008) is a sub-conference of the 18th European Conference on Artificial Intteligence (ECAI 2008) that will be held at the University of Patras, Greece, from July 21st to 25th.
:The presented paper will be soon available.

* '''International Joint Conference on Neural Networks (IJCNN 2007), Orlando, FL, USA'''
:'''Lung Cancer Identification by an Electronic Nose based on array of MOS Sensors''', Blatt Rossella, Bonarini Andrea, Calabrò Elisa, Della Torre Matteo, Matteucci Matteo, Pastorino Ugo. Proceedings of the 2007 International Joint Conference on Neural Networks (IJCNN 2007), Orlando, FL, USA: [[Special:IJCNNfinal.pdf|Paper-IJCNN2007]]

:Short presentation of the ''Lung Cancer Identification by an Electronic Nose based on an array of MOS Sensors'' paper: [[Special:LungCancerIdentificationIJCNN2007.pdf|Presentation-IJCNN2007]]

* '''International Workshop on Fuzzy Logic and Applications (WILF 2007), Ruta di Camogli, Genova, Italy'''

: '''Fuzzy k-NN Lung Cancer Identification by an Electronic Nose''', Blatt Rossella, Bonarini Andrea, Calabrò Elisa, Della Torre Matteo, Matteucci Matteo, Pastorino Ugo. Proceedings of the 7th International Workshop on Fuzzy Logic and Applications, WILF 2007, Lecture Notes in Computer Science (LNAI), LNAI 4578, pages 261-268, Springer. Camogli (GE), Italy, July 2007.

=== Description and results of experiments ===

=== Photos and videos ===

=== Link to source code of the software written for the project ===

=== Description and results of experiments ===

=== Useful internet links ===

Lung Cancer Detection by an Electronic Nose

2008-06-17T12:01:31Z

RossellaBlatt: /* Link to project documents and files */

== '''Part 1: project profile''' ==

=== Project name ===

Lung Cancer Detection by an Electronic Nose

=== Project short description ===

The electronic nose is an instrument able to detect and recognize odors, that is the volatile substances in the atmosphere or emitted by the analyzed substance. This device can react to a gas substance by providing signals that can be analyzed to classify the input. It is composed of a sensor array (6 MOS sensors, in our case) and a pattern classification process based on machine learning techniques.
In this project, we have been using an electronic nose based on an array of six MOS sensors, to recognize the presence of lung cancer in breaths' subjects, diagnosing the disease with a non invasive and low cost method.

During the first phase of our research, we have evaluated the possibility and accuracy of lung cancer diagnosis by classifying the
olfactory signal associated to exhalations of subjects.

At the end of the first phase, results have been very satisfactory and promising: we achieved an average accuracy of 92.6%, sensitivity of
95.3% and specificity of 90.5%. In particular we analyzed the breath of 101 individuals, of which 58 control subjects, and 43 suffer from
different types of lung cancer (primary and not) at different stages.
In order to find the components able to discriminate between the two classes ‘healthy’ and ‘sick’ at best, and to reduce the dimensionality
of the problem, we have extracted the most significant features and projected them into a lower dimensional space using Non Parametric
Linear Discriminant Analysis. Finally, we have used these features as input to several supervised pattern classification algorithms, based
on different k-nearest neighbors (k-NN) approaches (classic, modified and Fuzzy k-NN), linear and quadratic discriminant classifiers
and on a feed-forward artificial neural network (ANN). The observed results have all been validated using cross-validation.

These results pushed us to begin the second phase of the project, still in progress, to investigate the possibility of early lung cancer diagnosis: we are involving a larger number of subjects, partioned in different classes according to the type and stage of the disease. The research demonstrates that the electronic nose is a promising alternative to current lung cancer diagnostic techniques: the obtained predictive errors are lower than those achieved by present diagnostic methods, and the cost of the analysis, both in money, time and resources, is lower. The introduction of this technology will lead to very important social and business effects: its low price and small dimensions allow a large scale distribution, giving the opportunity to perform non invasive, cheap, quick, and massive early diagnosis and screening.

=== Dates ===
Start date: 2007/01/01

End date: --

=== Website(s) ===

At the moment no website avaible

=== People involved ===

===== Project head(s) =====

A. Bonarini - [[User:AndreaBonarini]]

M. Matteucci - [[User:MatteoMatteucci]]

===== Other Politecnico di Milano people =====

R. Blatt - [[User:RossellaBlatt]]

===== Students currently working on the project =====

Claudio Trameri - [[User:ClaudioTrameri]]

Mauro Verdirosa - [[User:MauroVerdirosa]]

===== Students who worked on the project in the past =====

===== External personnel: =====

Dott. Ugo Pastorino (Istituto dei Tumori - Milano)

Dott. Elisa Calabrò (Istituto dei Tumori - Milano)

Dott. Matteo Della Torre (SACMI - Imola)

=== Laboratory work and risk analysis ===

Laboratory work for this project will be mainly performed at the Istituto Nazionale dei Tumori di Milano, where the acquisistion of subjects' breath, both sick and healthy will be done.
For this kind of work, there are not potential risks.

== '''Part 2: project description''' ==

=== State of the art ===

=== Preliminary and sketches ===

=== Design notes and guidelines ===

=== Link to project documents and files ===

Results obtained from this work have been presented at different conferences:

* '''Prestigious Applications of Intelligent Systems (PAIS 2008), Patras, Greece'''
:The 5th Prestigious Applications of Intelligent Systems (PAIS 2008) is a sub-conference of the 18th European Conference on Artificial Intteligence (ECAI 2008) that will be held at the University of Patras, Greece, from July 21st to 25th.
:The presented paper will be soon available.

* '''International Joint Conference on Neural Networks (IJCNN 2007), Orlando, FL, USA'''
:'''Lung Cancer Identification by an Electronic Nose based on array of MOS Sensors''', Blatt Rossella, Bonarini Andrea, Calabrò Elisa, Della Torre Matteo, Matteucci Matteo, Pastorino Ugo. Proceedings of the 2007 International Joint Conference on Neural Networks (IJCNN 2007), Orlando, FL, USA: [[Image:IJCNNfinal.pdf|Paper-IJCNN2007]]

:Short presentation of the ''Lung Cancer Identification by an Electronic Nose based on an array of MOS Sensors'' paper: [[Special:LungCancerIdentificationIJCNN2007.pdf|Presentation-IJCNN2007]]

* '''International Workshop on Fuzzy Logic and Applications (WILF 2007), Ruta di Camogli, Genova, Italy'''

: '''Fuzzy k-NN Lung Cancer Identification by an Electronic Nose''', Blatt Rossella, Bonarini Andrea, Calabrò Elisa, Della Torre Matteo, Matteucci Matteo, Pastorino Ugo. Proceedings of the 7th International Workshop on Fuzzy Logic and Applications, WILF 2007, Lecture Notes in Computer Science (LNAI), LNAI 4578, pages 261-268, Springer. Camogli (GE), Italy, July 2007.

=== Description and results of experiments ===

=== Photos and videos ===

=== Link to source code of the software written for the project ===

=== Description and results of experiments ===

=== Useful internet links ===

Lung Cancer Detection by an Electronic Nose

2008-06-16T15:49:34Z

RossellaBlatt: /* Project short description */

== '''Part 1: project profile''' ==

=== Project name ===

Lung Cancer Detection by an Electronic Nose

=== Project short description ===

The electronic nose is an instrument able to detect and recognize odors, that is the volatile substances in the atmosphere or emitted by the analyzed substance. This device can react to a gas substance by providing signals that can be analyzed to classify the input. It is composed of a sensor array (6 MOS sensors, in our case) and a pattern classification process based on machine learning techniques.
In this project, we have been using an electronic nose based on an array of six MOS sensors, to recognize the presence of lung cancer in breaths' subjects, diagnosing the disease with a non invasive and low cost method.

During the first phase of our research, we have evaluated the possibility and accuracy of lung cancer diagnosis by classifying the
olfactory signal associated to exhalations of subjects.

At the end of the first phase, results have been very satisfactory and promising: we achieved an average accuracy of 92.6%, sensitivity of
95.3% and specificity of 90.5%. In particular we analyzed the breath of 101 individuals, of which 58 control subjects, and 43 suffer from
different types of lung cancer (primary and not) at different stages.
In order to find the components able to discriminate between the two classes ‘healthy’ and ‘sick’ at best, and to reduce the dimensionality
of the problem, we have extracted the most significant features and projected them into a lower dimensional space using Non Parametric
Linear Discriminant Analysis. Finally, we have used these features as input to several supervised pattern classification algorithms, based
on different k-nearest neighbors (k-NN) approaches (classic, modified and Fuzzy k-NN), linear and quadratic discriminant classifiers
and on a feed-forward artificial neural network (ANN). The observed results have all been validated using cross-validation.

These results pushed us to begin the second phase of the project, still in progress, to investigate the possibility of early lung cancer diagnosis: we are involving a larger number of subjects, partioned in different classes according to the type and stage of the disease. The research demonstrates that the electronic nose is a promising alternative to current lung cancer diagnostic techniques: the obtained predictive errors are lower than those achieved by present diagnostic methods, and the cost of the analysis, both in money, time and resources, is lower. The introduction of this technology will lead to very important social and business effects: its low price and small dimensions allow a large scale distribution, giving the opportunity to perform non invasive, cheap, quick, and massive early diagnosis and screening.

=== Dates ===
Start date: 2007/01/01

End date: --

=== Website(s) ===

At the moment no website avaible

=== People involved ===

===== Project head(s) =====

A. Bonarini - [[User:AndreaBonarini]]

M. Matteucci - [[User:MatteoMatteucci]]

===== Other Politecnico di Milano people =====

R. Blatt - [[User:RossellaBlatt]]

===== Students currently working on the project =====

Claudio Trameri - [[User:ClaudioTrameri]]

Mauro Verdirosa - [[User:MauroVerdirosa]]

===== Students who worked on the project in the past =====

===== External personnel: =====

Dott. Ugo Pastorino (Istituto dei Tumori - Milano)

Dott. Elisa Calabrò (Istituto dei Tumori - Milano)

Dott. Matteo Della Torre (SACMI - Imola)

=== Laboratory work and risk analysis ===

Laboratory work for this project will be mainly performed at the Istituto Nazionale dei Tumori di Milano, where the acquisistion of subjects' breath, both sick and healthy will be done.
For this kind of work, there are not potential risks.

== '''Part 2: project description''' ==

=== State of the art ===

=== Preliminary and sketches ===

=== Design notes and guidelines ===

=== Link to project documents and files ===

Results obtained from this work have been presented at different conferences:

* '''Prestigious Applications of Intelligent Systems (PAIS 2008), Patras, Greece'''
:The 5th Prestigious Applications of Intelligent Systems (PAIS 2008) is a sub-conference of the 18th European Conference on Artificial Intteligence (ECAI 2008) that will be held at the University of Patras, Greece, from July 21st to 25th.
:The presented paper will be soon available.

* '''International Joint Conference on Neural Networks (IJCNN 2007), Orlando, FL, USA'''
:'''Lung Cancer Identification by an Electronic Nose based on array of MOS Sensors''', Blatt Rossella, Bonarini Andrea, Calabrò Elisa, Della Torre Matteo, Matteucci Matteo, Pastorino Ugo. Proceedings of the 2007 International Joint Conference on Neural Networks (IJCNN 2007), Orlando, FL, USA: [[Special:IJCNNfinal.pdf|Paper-IJCNN2007]]

:Short presentation of the ''Lung Cancer Identification by an Electronic Nose based on an array of MOS Sensors'' paper: [[Special:LungCancerIdentificationIJCNN2007.pdf|Presentation-IJCNN2007]]

* '''International Workshop on Fuzzy Logic and Applications (WILF 2007), Ruta di Camogli, Genova, Italy'''

: '''Fuzzy k-NN Lung Cancer Identification by an Electronic Nose''', Blatt Rossella, Bonarini Andrea, Calabrò Elisa, Della Torre Matteo, Matteucci Matteo, Pastorino Ugo. Proceedings of the 7th International Workshop on Fuzzy Logic and Applications, WILF 2007, Lecture Notes in Computer Science (LNAI), LNAI 4578, pages 261-268, Springer. Camogli (GE), Italy, July 2007.

=== Description and results of experiments ===

=== Photos and videos ===

=== Link to source code of the software written for the project ===

=== Description and results of experiments ===

=== Useful internet links ===

Brain-Computer Interface

2008-06-09T14:45:00Z

RossellaBlatt:

A Brain-Computer Interface (BCI) is an experimental communication system that allows an individual to control a device by using signals from the brain (e.g., electroencephalography -- EEG).

You can find a longer description on the [http://airlab.elet.polimi.it/index.php/airlab/research_areas/biosignal_analysis?z=2299 AIRLab page].

The BCI project is in the [[BioSignal_Analysis]] area.

== Ongoing projects ==

* GA for feature extraction
* [[Online P300 and ErrP recognition with BCI2000]] (Master thesis, Andrea Sgarlata).
* [[BCI based on Motor Imagery]]
** [[Sensorymotor Rhythms Detection and Control]] (Master thesis, Tiziano D'Albis; Bachelor thesis, Fabio Beltramini)
** [[Feature Selection and Extraction for a BCI based on motor imagery]] (Master thesis, Francesco Amenta)
* [[Graphical user interface for an autonomous wheelchair]] (Roberto Massimini)

== Finished projects ==

* Tesi di Carlo Gimondi e Luisella Messana
* Tesi di Gianmaria Visconti
* Tesi di Francesco Cartella