A Single-Channel SSVEP-Based BCI Speller Using Deep Learning

Abstract

This paper aims to develop a speller system based on a bipolar single-channel electroencephalogram with sufficient accuracy. The proposed system consists of a custom-designed headset, a new virtual keyboard with 58 characters, special symbols, and digits, and a five-target steady-state visual-evoked potential (SSVEP)-based brain-computer interface (BCI) utilizing one-dimensional convolutional neural network (1-D CNN) for SSVEP frequency detection. The deep learning model is implemented and trained under the training mode before being applied in the operation mode of the system. To validate the proposed model, we acquire the training dataset with numerous testing conditions, including different frequency resolutions of the feature and different time-window lengths of analysis. Two types of features based on the frequency domain are investigated to compare their performances in terms of classification accuracy of the model. The experimental results from eight subjects shows that on average, the proposed model can classify five-class SSVEP data with a high accuracy of 99.2%. The proposed BCI is then employed in an online experiment of spelling the word ''SPELLER'' using 2-s time window. Consequently, the system achieves an average accuracy of 97.4% and an information transfer rate of 49 ± 7.7 bpm, showing the practicality and feasibility of implementing a reliable single-channel SSVEP-based speller utilizing 1-D CNN. INDEX TERMSBrain-computer interface (BCI), electroencephalogram (EEG), bipolar single channel, speller, one-dimensional convolutional neural network (1-D CNN), steady-state visual evoked potential (SSVEP).

Wink based facial expression classification using machine learning approach

“…In a BCI system, specific patterns of brain activity are translated into control commands in the purpose of particular devices operation [2]. Mind-controlled wheelchair [3], home appliances [4], prosthetic arm controlling [5], spelling system [6], emotion detection system [7] and biometrics [8] are the popular BCI applications [9]. Currently, BCI applications have been widened from medical to non-medical fields, for example, BCI based games and virtual reality [9].…”

Redes Neurais Profundas Triplet Aplicadas à Classificação de Sinais em Interfaces Cérebro-Computador

“…Trabalhos recentes, que abordam a aplicação de redes neurais profundas ao problema de classificação de sinais SSVEP, utilizam arquiteturas convolucionais e entropia cruzada como função de custo. Em [5] e [6] os autores demonstram queé possível construir um teclado virtual treinando redes neurais convolucionais profundas e considerando um dos sujeitos como o de teste. Mas, até o momento, não há na literatura um estudo de classificação de sinais SSVEP utilizando redes neurais profundas triplet.…”

Decoding Visual Responses based on Deep Neural Networks with Ear-EEG Signals

“…Since these artifacts are much larger in amplitude than the brain signal that contains the user's intent, it is difficult to catch properly the meaning of intention. Therefore, decoding human intention in the ambulatory environment is tried these days [4][5][6][7] using movement artifact removal methods [8,9] and deep neural networks [7,[10][11][12][13][14][15][16][17] to robust the artifacts and increase the performance.…”

“…This paper experimented in ambulatory conditions as well, riding exoskeleton. Another study [10] also used the features based on fast Fourier transform and CNN classifiers to recognize the human intention from SSVEP. The Castermans et al [4] classified ERP intention in the ambulatory environment, up to 1.25 m/s using linear discriminant analysis (LDA) classifier.…”