Brain Machine Interface — IEETA case study

Author

Georgieva, Petia ; Silva, Filipe ; Figueiredo, Nuno

Author_Institution

Dept. of Electron. Telecommun. & Inf. (DETI), Univ. of Aveiro, Aveiro, Portugal

fYear

2012

fDate

6-8 Sept. 2012

Firstpage

374

Lastpage

379

Abstract

The goal of the present paper is to report the recent advances in Electroencephalogram (EEG)-based Brain Machine Interface (BMI) developed at the Institute of Electrical Engineering and Telematics of Aveiro (IEETA). First, a short overview of the most successful BMI technologies is presented and then our ongoing research and protocol for motor imagery noninvasive BMI for a mobile robot control is discussed. The main EEG signal processing challenges as filtering, feature extraction and classification are also considered.

Keywords

brain-computer interfaces; electroencephalography; feature extraction; medical signal processing; mobile robots; path planning; signal classification; BMI technologies; EEG signal processing; EEG-based BMI; IEETA; Institute of Electrical Engineering and Telematics of Aveiro; electroencephalogram-based brain machine interface; feature extraction; mobile robot control; motor imagery noninvasive BMI; signal classification; signal filtering; Brain; Electrodes; Electroencephalography; Feature extraction; Neurons; Scalp; Visualization; EEG features extraction and classification; brain machine interface; motor imagery;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems (IS), 2012 6th IEEE International Conference

Conference_Location

Sofia

Print_ISBN

978-1-4673-2276-8

Type

conf

DOI

10.1109/IS.2012.6335164

Filename

6335164