A novel Deep Capsule Neural Network for Vowel Imagery patterns from EEG signals

A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or ‘locked in’ by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.

We devised spatial filters for multi-channel EEG that lead to signals which discriminate optimally between two conditions. We demonstrate the effectiveness of this method by classifying single-trial EEGs, recorded during preparation for movements of the left or right index finger or the right foot. The classification rates for 3 subjects were 94, 90 and 84%, respectively. The filters are estimated from a set of multichannel EEG data by the method of Common Spatial Patterns, and reflect the selective activation of cortical areas. By construction, we obtain an automatic weighting of electrodes according to their importance for the classification task. Computationally, this method is parallel by nature, and demands only the evaluation of scalar products. Therefore, it is well suited for on-line data processing. The recognition rates obtained with this relatively simple method are as good as, or higher than those obtained previously with other methods. The high recognition rates and the method's procedural and computational simplicity make it a particularly promising method for an EEG-based brain-computer interface.