The contribution describes the design, optimization and verification of the off-line single-trial movement classification system. Four types of movements are used for the classification: the right index finger extension vs. flexion as well as the right shoulder (proximal) vs. right index finger (distal) movement. The classification system utilizes hidden information stored in the characteristic...

Based on recent electroencephalography (EEG) and near-infrared spectroscopy (NIRS) studies that showed that tasks such as motor imagery and mental arithmetic induce specific neural response patterns, we propose a hybrid brain-computer interface (hBCI) paradigm in which EEG and NIRS data are fused to improve binary classification performance. We recorded simultaneous NIRS-EEG data from nine part...

Electroencephalogram (EEG) signal classification is used in many applications. Typically, this implemented based on methods which consist of two steps. These steps are known as the step preprocessing and classification. The transforms initial into attributes. According to several studies, transformation can result loss some useful information and, consequently, formed attributes uncertain. This...

Constructing non-invasive Brain-Computer Interfaces (BCI) that are practical for use in assistive technology has proven to be a challenging problem. We assert that classification algorithms that are capable of capturing sophisticated spatiotemporal patterns in Electroencephalography (EEG) signals are necessary in order for BCI to deliver fluid and reliable control. Since Echo State Networks (ES...

The development of an electroencephalograph (EEG)-based brain-computer interface (BCI) requires rapid and reliable discrimination of EEG patterns, e.g., associated with imaginary movement. One-sided hand movement imagination results in EEG changes located at contra- and ipsilateral central areas. We demonstrate that spatial filters for multichannel EEG effectively extract discriminatory informa...

One of the challenges in modeling cognitive events from electroencephalogram (EEG) data is finding representations that are invariant to interand intra-subject differences, as well as to inherent noise associated with EEG data collection. Herein, we propose a novel approach for learning such representations from multichannel EEG time-series, and demonstrate its advantages in the context of ment...