Next Article in Journal
Implementation of a PSO-Based Security Defense Mechanism for Tracing the Sources of DDoS Attacks
Previous Article in Journal
Network Intrusion Detection with a Hashing Based Apriori Algorithm Using Hadoop MapReduce
Previous Article in Special Issue
Statistical-Hypothesis-Aided Tests for Epilepsy Classification
Open AccessReview

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;
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 (MLESP 2018))
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 EEG signals; neural net; BCI; learning; classification
Show Figures

Figure 1

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop