In this position paper, we investigate whether a parallel factor analysis (Parafac) decomposition is beneficial to the decoding of steady-state visual evoked potentials (SSVEP) present in electroencephalogram (EEG) recordings taken from the subject’s scalp. In particular, we develop an automatic algorithm aimed at detecting the stimulation frequency after Parafac decomposition. The results are ...

Steady-state visually evoked potentials (SSVEP) can be elicited by a large variety of stimuli. To the best of our knowledge, the size and shape effect of stimuli has never been investigated in the literature. We study the relationship between the visual parameters (size and shape) of the stimulation and the resulting brain response. A tentative physiological interpretation is proposed and the p...

In SSVEP based BCIs, visual stimulus modulated at different frequencies are simultaneously presented to the user. Each pattern is associated with an action in an output (active) device. When the user focuses his/her attention on a certain pattern, the corresponding stimulating frequency (or its harmonics) dominantly appears in the spectral representation of the EEG signals recorded at occipital...

Abstract Brain computer interface (BCI) systems have been regarded as a new way of communication for humans. In this research, common methods such wavelet transform are applied in order to extract features. However, genetic algorithm (GA), an evolutionary method, is used select Finally, classification was done using the two approaches support vector machine (SVM) and Bayesian method. Five featu...

Este artículo presenta la integración de señales electromiográficas (EMG) y una interfaz cerebro-computadora (BCI) basada en electroencefalográficas (EEG) un diseño domótico asistencia. La EMG utiliza señal miográfica extraída del tibial anterior dos secciones: músculo activo reposo. BCI el potencial visual evocado estado estable (SSVEP) generado respuesta a cuadros parpadeantes. Se empleó méto...

Objective: We used deep convolutional neural networks (DCNNs) to classify electroencephalography (EEG) signals in a steady-state visually evoked potentials (SSVEP) based single-channel brain-computer interface (BCI), which does not require calibration on the user. Methods: EEG were converted spectrograms and served as input train DCNNs using transfer learning technique. also modified applied da...