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Brain-Computer Interfacing

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#40362 / #8

Seit WiSe 2022/23

English

Brain-Computer Interfacing

9

Blankertz, Benjamin

Benotet

Portfolioprüfung

English

Zugehörigkeit


Fakultät IV

Institut für Softwaretechnik und Theoretische Informatik

34355200 FG S-Professur Neurotechnologie

Keine Angabe

Kontakt


MAR 4-3

Wagner vom Berg, Gabriel Leander

contact@neuro.tu-berlin.de

https://www.tu.berlin/neuro/studium-lehre/lehrveranstaltungen

Lernergebnisse

Students know the essential concepts of Brain-Computer Interfacing (BCI). They are capable of applying methods of biomedical signal processing and single-trial classification to neural data. They can provide an interpretation of the outcome of their analysis in a statistical as well as in a neurophysiological manner. Moreover, they are aware of potential issues imposed by machine learning applications, e.g., due to biases in the database. Through the seminar, they have more profound knowledge about special topics of BCI research in data analysis of physiological signals. Regarding methodology, independent of subject specific content, the students - are able to research sources and evaluate them reflectively - are able to present scientific topics in front of an audience and to discuss them critically - know presentation techniques in order to present the content of a lecture as clearly and comprehensibly as possible - have the ability to elaborate subject content in written form according to scientific standards - are able to manage their time sensibly

Lehrinhalte

IL: Approaches to Brain-Computer Interfacing (BCI); Event-related potentials (ERPs); Spatial filters; Multivariate analysis of brain signals; Single-trial classification of spatio-temporal features; Regularized discriminant analysis (RDA); The linear model (forward and backward) of EEG; Interpretation of spatial patterns and filters; Modulation of spontaneous brain rhythms; Event-related synchronization and desynchronization (ERS, ERD); Common spatial pattern (CSP) Analysis; Classification of spatio-spectral features; Signal decomposistion methods; Supervised and unsupervised methods of adaptation in the classification of EEG; Experimental design SE: Literature search, presentation techniques; Examplary topics: Neural correlates of attention in free viewing, Predictors of BCI Performance, Co-adaptive Systems, Control by Spatial Attention; Hybrid BCIs, Multimodal BCIs

Modulbestandteile

Compulsory area

Die folgenden Veranstaltungen sind für das Modul obligatorisch:

LehrveranstaltungenArtNummerTurnusSpracheSWS ISIS VVZ
Brain-Computer InterfacingIV3435 L 501WiSeKeine Angabe4
Current Topics in Brain-Computer InterfacingSEM3435 L 502WiSeKeine Angabe2
ISIS-Kurssuche nach Veranstaltungen des Moduls

Arbeitsaufwand und Leistungspunkte

Brain-Computer Interfacing (IV):

AufwandbeschreibungMultiplikatorStundenGesamt
Solving assignments10.06.0h60.0h
Attendance15.04.0h60.0h
Pre-/post-processing15.02.0h30.0h
Preparing for written tests2.015.0h30.0h
180.0h(~6 LP)

Current Topics in Brain-Computer Interfacing (SEM):

AufwandbeschreibungMultiplikatorStundenGesamt
Attendance, presentations, discussions10.01.5h15.0h
Pre/post-processing1.075.0h75.0h
90.0h(~3 LP)
Der Aufwand des Moduls summiert sich zu 270.0 Stunden. Damit umfasst das Modul 9 Leistungspunkte.

Beschreibung der Lehr- und Lernformen

The integrated lecture (IL) consists of a lecture (mainly teacher-centered, with some period of group work) and assignments. The latter require independently solving programming exercises and working on complex tasks under guidance of a tutor. After an introduction to literature research, participants choose topics and presentation formats, e.g. talk, wiki articles, screencast video. For an early overview of the topic, each participant gives a one-minute presentation. After further familiarization with the topic, spotlight presentations (about 5 minutes) are given. Upon completion of the main presentation, participants will write comments on selected presentations by their peers. Seminar presentations are developed under the guidance of a supervisor.

Voraussetzungen für die Teilnahme / Prüfung

Wünschenswerte Voraussetzungen für die Teilnahme an den Lehrveranstaltungen:

* Mandatory: programming skills in python; background in mathematics, in particular linear algebra and probability theory. * Helpful, but not obligatory: Basic knowledge in signal processing and machine learning.

Verpflichtende Voraussetzungen für die Modulprüfungsanmeldung:

Dieses Modul hat keine Prüfungsvoraussetzungen.

Abschluss des Moduls

Benotung

Benotet

Prüfungsform

Portfolio examination

Art der Portfolioprüfung

100 Punkte pro Element

Sprache(n)

English

Prüfungselemente

NameGewichtKategorieDauer/Umfang
Deliverable assessment: 10 Assignments15praktisch6h each
Examination: 2 written tests60schriftlichjeweils 60 min
Deliverable assessment: Presentations in the seminar25flexibel30 min or 8 pages

Notenschlüssel

Notenschlüssel »Notenschlüssel 2: Fak IV (2)«

Gesamtpunktzahl1.01.31.72.02.32.73.03.33.74.0
100.0pt95.0pt90.0pt85.0pt80.0pt75.0pt70.0pt65.0pt60.0pt55.0pt50.0pt

Prüfungsbeschreibung (Abschluss des Moduls)

The grade is determined according to § 47 (2) AllgStuPO with the grading system 2 of faculty IV. * Exercises: Concurrent to the lecture, there will be a tutorial in which ten assignment sheets have to be solved. These are devoted to practical EEG analysis (programming). * Written exams: In the first half and in the second half of the lecture, there will be a written test of about 60 minutes. * Seminar presentation: Presentation of a research topic (various formats: orally with slides or written report or wiki articles or screencast video; options may differ from year to year);moreover: short presentations, contributions to discussion, commentaries

Dauer des Moduls

Für Belegung und Abschluss des Moduls ist folgende Semesteranzahl veranschlagt:
1 Semester.

Dieses Modul kann in folgenden Semestern begonnen werden:
Wintersemester.

Maximale teilnehmende Personen

Die maximale Teilnehmerzahl beträgt 25.

Anmeldeformalitäten

Registration is not required, but stating the interest to participate in the lecture is welcome for the planning of resources. * Either email to Sekr. MAR 4-3: Imke Weitkamp <imke.weitkamp@tu-berlin.de> * or register in the respective courses in the information system at https://isis.tu-berlin.de/

Literaturhinweise, Skripte

Skript in Papierform

Verfügbarkeit:  nicht verfügbar

 

Skript in elektronischer Form

Verfügbarkeit:  verfügbar
Zusätzliche Informationen:

 

Literatur

Empfohlene Literatur
Blankertz B, Lemm S, Treder MS, Haufe S, Müller KR, Single-trial analysis and classification of ERP components - a tutorial, Neuroimage, 56:814-825, 2011.
Blankertz B, Tomioka R, Lemm S, Kawanabe M, Müller KR, Optimizing Spatial Filters for Robust EEG Single-Trial Analysis, IEEE Signal Process Mag, 25(1):41-56, 2008.
Dornhege G, R. Millán J d, Hinterberger T, McFarland D, Müller K (eds), Toward Brain-Computer Interfacing, MIT Press, 2007.
Parra LC, Spence CD, Gerson AD, Sajda P. Recipes for the Linear Analysis of EEG, Neuroimage, 28(2):326-341, 2005.

Zugeordnete Studiengänge


Diese Modulversion wird in folgenden Studiengängen verwendet:

Studiengang / StuPOStuPOsVerwendungenErste VerwendungLetzte Verwendung
Biomedizinische Technik (M. Sc.)16WiSe 2022/23SoSe 2025
Computer Engineering (M. Sc.)124WiSe 2022/23SoSe 2025
Computer Science (Informatik) (M. Sc.)112WiSe 2022/23SoSe 2025
Elektrotechnik (M. Sc.)118WiSe 2022/23SoSe 2025
Wirtschaftsingenieurwesen (M. Sc.)112WiSe 2022/23SoSe 2025

Sonstiges

Keine Angabe