1. Modulverzeichnis (MTS)
  2. Module
  3. Modulbeschreibung anzeigen

Project: Brain-Computer Interfacing

Display language
To modulepage Generate PDF

#40007 / #2

SS 2016 - WS 2016/17

English

Project: Brain-Computer Interfacing

9

Blankertz, Benjamin

benotet

Portfolioprüfung

Zugehörigkeit


Fakultät IV

Institut für Softwaretechnik und Theoretische Informatik

34355200 FG S-Professur Neurotechnologie

No information

Kontakt


MAR 4-3

Blankertz, Benjamin

benjamin.blankertz@tu-berlin.de

http://wiki.ml.tu-berlin.de/wiki/NT/Courses

Learning Outcomes

Students having successfully participate in this module are capable of independently - pursue investigations in the field of EEG analysis that are relevant for Brain-Computer Interface research, - plan and conduct the required experimental studies, - evaluate the acquired data using statistical methods, and - interpret the results and present them in a scientific way. This course conveys predominantly professional (25%), methods (50%), social (20%), systemic (5%) skills.

Content

This projects conveys experimental competences of neurotechnology exemplary. Moreover, theoretical skills in signal processing and machine learning are trained practically in hands-on experiments with self acquired data. A typical project is as follows: work out an experimental design for a given hypothesis; implement the experiment and conduct a study (including the acquisition of physiological data) with about 6 participants; investigate the acquire data with standard analysis methods and techniques from machine learning; put the results into perspective given the state-of-the-art and present them as a talk in a written report. Projects are performed in groups of 2 to 4 students. Note: This project is about the practical aspects of Brain-Computer Interfaces. The background about this field of research is given in the lecture "Brain-Computer Interfacing".

Module Components

Pflichtteil:

All Courses are mandatory.

Course NameTypeNumberCycleLanguageSWSVZ
Brain-Computer InterfacingPJ3435 L 504SoSeNo information6

Workload and Credit Points

Brain-Computer Interfacing (PJ):

Workload descriptionMultiplierHoursTotal
Ausarbeitung der Ergebnisse (Referat/schriftliche Ausarbeitung)1.040.0h40.0h
Experimente vorbereiten und durchführen (mit Betreuung; gruppenweise!)16.05.0h80.0h
Individual- und Gruppenarbeit (Datenanalyse)1.0100.0h100.0h
Individual- und Gruppenarbeit (Recherche, Konzepte)1.038.0h38.0h
Präsenzzeit (Plenumteile)6.02.0h12.0h
270.0h(~9 LP)
The Workload of the module sums up to 270.0 Hours. Therefore the module contains 9 Credits.

Description of Teaching and Learning Methods

Some teacher centered introduction to BCI studies, presentation of project topics and introduction to the computational investigation of neural data. The main part of the course is done in group work (2-4 students) with a specialization of each group member: Literature recherche, clarification of the point of investigation (hypotheses), planning and conducting an EEG- or NIRS-study (about 6 participants) and possibly additionally a behavioral experiment; independent investigation of the acquired data using algorithms of signal processing and machine learning; presentation and discussion of results. --- --- --- Frontalunterricht im Plenum: Einführung in die Thematik; Vorstellung der zur Auswahl stehenden Themen; Einführung in die computergestützte Auswertung neuronaler Daten. In kleinen Arbeitsgruppen (2-4 Personen) aber mit Spezialisierung der Gruppenmitglieder: Literaturarbeit, Präzisierung der vorgegebenen Fragestellung, Planung und Durchführung eines EEG- oder NIRS-Experiments (ca. 6 Probanden) und ggf. eines Verhaltensexperiments unter Anleitung eines Assistenten; selbstständige Auswertung der gewonnenen Daten mit Hilfe von Algorithmen der digitalen Signalverarbeitung und des Maschinellen Lernens; adäquate Präsentation und Diskussion der erzielten Resultate im Plenum.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

Programming skills (Matlab/Octave) and basic knowledge in signal processing and machine learning is required. Having participated in the lecture Brain-Computer Interfacing before is helpful but not required. --- --- -- Programmierfähigkeit (Matlab/Octave) und Grundkenntnisse aus dem Bereich Signalverarbeitung und Klassifikation werden vorausgesetzt. Die voherige Teilnahme an der Vorlesung Brain-Computer Interfacing ist hilfreich.

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

No information

Language

English

Test elements

NamePoints/WeightCategorieDuration/Extent
(Deliverable assessment) Code for data analysis and stimulus presentation20No informationNo information
(Deliverable assessment) Presentation of results as a talk20No informationNo information
(Deliverable assessment) Written report20No informationNo information
(Learning process review) Practical laboratory work including protocols40No informationNo information

Grading scale

No information

Test description (Module completion)

The grade is determined according to § 47 (2) AllgStuPO with the grading system 1 of faculty IV. Code for data analysis and stimulus presentation (20P): The code is evaluated according to programming style, effectiveness and documentation. Practical laboratory work including protocols (40P): The practical work is assessed according to the quality of the acquired signals; the interaction with the participants; the elaborateness in the usage of equipment; the protocols Presentation of results as a talk (20P): The talk is assessed according to content, clarity in structure, clear design of slides, contact to listeners, way of speaking, appropriate answering of questions. Written report (20P): The report is evaluated according to the criteria for a scientific paper with the structure: Introduction to the field including a review of state-of-the-art; Description of experimental design and hypotheses; Description of Material and Methods; Presentation of results and discussion; Conclusion.

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:
Sommersemester.

Maximum Number of Participants

The maximum capacity of students is 20.

Registration Procedures

Registration at the secretary MAR 4-3: Imke Weitkamp <imke.weitkamp@tu-berlin.de>, room 4.042 in the building Marchstr. 23.

Recommended reading, Lecture notes

Lecture notes

Availability:  unavailable

 

Electronical lecture notes

Availability:  unavailable

 

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.
Wolpaw JR and Wolpaw LW (eds), Brain-Computer Interfaces - Principles and Practice, Oxford University Press 2012.

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.

Masterstudiengang Informatik: Studienschwerpunkt Intelligente Systeme Masterstudiengang Technische Informatik: Studienschwerpunkt Informationssysteme. Masterstudiengang Elektrotechnik: Studienschwerpunkt Informationstechnologie. Bei ausreichenden Kapazitäten auch als Wahlpflichtmodul in anderen Studiengängen (vor allem aus dem natur- und ingenieurswissenschaftlichen Bereich und der Mathematik).

Miscellaneous

No information