Neural Net-Based Approach to EEG Signal Acquisition and Classification in BCI Applications
LIASD Lab., Department of Computer Science, University of Paris 8, 93526 Saint-Denis, France
*
Author to whom correspondence should be addressed.
Computers 2019, 8(4), 87; https://doi.org/10.3390/computers8040087
Received: 15 October 2019
/
Revised: 1 December 2019
/
Accepted: 2 December 2019
/
Published: 4 December 2019
(This article belongs to the Special Issue Machine Learning for EEG Signal Processing)
The following contribution describes a neural net-based, noninvasive methodology for electroencephalographic (EEG) signal classification. The application concerns a brain–computer interface (BCI) allowing disabled people to interact with their environment using only brain activity. It consists of classifying user’s thoughts in order to translate them into commands, such as controlling wheelchairs, cursor movement, or spelling. The proposed method follows a functional model, as is the case for any BCI, and can be achieved through three main phases: data acquisition and preprocessing, feature extraction, and classification of brains activities. For this purpose, we propose an interpretation model implementing a quantization method using both fast Fourier transform with root mean square error for feature extraction and a self-organizing-map-based neural network to generate classifiers, allowing better interpretation of brain activities. In order to show the effectiveness of the proposed methodology, an experimental study was conducted by exploiting five mental activities acquired by a G.tec BCI system containing 16 simultaneously sampled bio-signal channels with 24 bits, with experiments performed on 10 randomly chosen subjects.
View Full-Text
Keywords:
EEG signals; neural net; BCI; learning; classification
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Chenane, K.; Touati, Y.; Boubchir, L.; Daachi, B. Neural Net-Based Approach to EEG Signal Acquisition and Classification in BCI Applications. Computers 2019, 8, 87. https://doi.org/10.3390/computers8040087
AMA Style
Chenane K, Touati Y, Boubchir L, Daachi B. Neural Net-Based Approach to EEG Signal Acquisition and Classification in BCI Applications. Computers. 2019; 8(4):87. https://doi.org/10.3390/computers8040087
Chicago/Turabian StyleChenane, Kathia, Youcef Touati, Larbi Boubchir, and Boubaker Daachi. 2019. "Neural Net-Based Approach to EEG Signal Acquisition and Classification in BCI Applications" Computers 8, no. 4: 87. https://doi.org/10.3390/computers8040087
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
