Display language
To modulepage Generate PDF

#40515 / #5

SS 2017 - WS 2017/18

English

Brain-Computer Interfacing (basic)

6

Blankertz, Benjamin

benotet

Portfolioprüfung

Zugehörigkeit


Fakultät IV

Institut für Softwaretechnik und Theoretische Informatik

34355200 FG S-Professur Neurotechnologie

No information

Kontakt


MAR 4-3

Miklody, Daniel

benjamin.blankertz@tu-berlin.de

Learning Outcomes

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.

Content

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

Module Components

Pflichtteil:

All Courses are mandatory.

Course NameTypeNumberCycleLanguageSWSVZ
Brain-Computer InterfacingIV3435 L 501WiSeNo information4

Workload and Credit Points

Brain-Computer Interfacing (IV):

Workload descriptionMultiplierHoursTotal
Bearbeitung der Übungsaufgaben10.06.0h60.0h
Präsenzzeit15.04.0h60.0h
Vor-/Nachbereitung15.02.0h30.0h
Vorbereitung für die Prüfungen2.015.0h30.0h
180.0h(~6 LP)
The Workload of the module sums up to 180.0 Hours. Therefore the module contains 6 Credits.

Description of Teaching and Learning Methods

This integrated course consists of a lecture (mainly teacher-centred, 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. --- Die Integrierte Lehrveranstaltung besteht aus einem Vorlesungsteil (Frontalunterricht vor allen Teilnehmern zur Vermittlung des Stoffes; einige Phasen Gruppenarbeit) und einem Anteil praktischer Arbeit. Letztere besteht aus dem selbstständigen Bearbeiten von Übungsaufgaben und der Bearbeitung einer komplexeren Fragestellung unter Anleitung eines Assistenten.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

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

Mandatory requirements for the module test application:

This module has no requirements.

Module completion

Grading

graded

Type of exam

Portfolio examination

Type of portfolio examination

100 Punkte pro Element

Language

English

Test elements

NameWeightCategorieDuration/Extent
(Ergebnisprüfung): 10 Assignments / Hausaufgaben (Übungszettel)15practicaljeweils 6h
(Punktuelle Leistungsabfrage): 2 written exams / schriftliche Tests60writtenjeweils 45 min

Grading scale

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

Test description (Module completion)

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 eight 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 written tests of 45 minutes each.

Duration of the Module

The following number of semesters is estimated for taking and completing the module:
1 Semester.

This module may be commenced in the following semesters:
Wintersemester.

Maximum Number of Participants

This module is not limited to a number of students.

Registration Procedures

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/

Recommended reading, Lecture notes

Lecture notes

Availability:  unavailable

 

Electronical lecture notes

Availability:  available
Additional information:
Skripte in elektronischer Form werden jeweils nach der Vorlesung zur Verfügung gestellt.

 

Literature

Recommended literature
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.

Assigned Degree Programs


This module is used in the following Degree Programs (new System):

Studiengang / StuPOStuPOsVerwendungenErste VerwendungLetzte Verwendung
This module is not used in any degree program.

Students of other degrees can participate in this module without capacity testing.

Miscellaneous

No information