Steady-State Visual Evoked Potential Classification Using Complex Valued Convolutional Neural Networks

The steady-state visual evoked potential (SSVEP), which is a kind of event-related potential in electroencephalograms (EEGs), has been applied to brain–computer interfaces (BCIs). SSVEP-based BCIs currently perform the best in terms of information transfer rate (ITR) among various BCI implementation methods. Canonical component analysis (CCA) or spectrum estimation, such as the Fourier transform, and their extensions have been used to extract features of SSVEPs. However, these signal extraction methods have a limitation in the available stimulation frequency; thus, the number of commands is limited. In this paper, we propose a complex valued convolutional neural network (CVCNN) to overcome the limitation of SSVEP-based BCIs. The experimental results demonstrate that the proposed method overcomes the limitation of the stimulation frequency, and it outperforms conventional SSVEP feature extraction methods

An N100-P300 Spelling Brain-Computer Interface with Detection of Intentional Control

A brain-computer interface (BCI) is a tool to communicate with a computer via brain signals without the user making any physical movements, thus enabling disabled people to communicate with their environment and with others. P300-based ERP spellers are a widely used spelling visual BCI using the P300 component of event-related potential (ERP). However, they have a technical problem in that at least 2 N flashes are required to present N characters. This prevents the improvement of accuracy and restricts the typing speed. To address this issue, we propose a method that uses N100 in addition to P300. We utilize novel stimulus images to detect the user’s gazing position by using N100. By using both P300 and N100, the proposed visual BCI reduces the number of flashes and improves the accuracy of the P300 speller. We also propose using N100 to classify non-control (NC) and intentional control (IC) states. In our experiments, the detection accuracy of N100 was significantly higher than that of P300 and the proposed method exhibited a higher information transfer rate (ITR) than the P300 speller

A Flexible Method for Envelope Estimation in Empirical Mode Decomposition

Abstract. A flexible and efficient method for finding the envelope within the empirical mode decomposition (EMD) is introduced. Unlike the existing (deterministic) spline based strategy, the proposed enve-lope is a result of an optimisation precess and sought as a minimum of a quadratic cost function. A closed form solution of this optimisation prob-lem is obtained and it is shown that by choosing free parameters, we can fine-tune the frequency resolution or the number of intrinsic mode func-tions (IMFs) as well as the shape of the envelopes. Computer simulations on both the synthetic and real-world electro-encephalogram (EEG) data support the analysis.