Regularized common spatial patterns with subject-to-subject transfer of EEG signals

A subject transfer framework for EEG classification

This paper proposes a subject transfer framework for EEG classification. It aims to improve the classification performance when the training set of the target subject (namely user) is small owing to the need to reduce the calibration session. Our framework pursues improvement not only at the feature extraction stage, but also at the classification stage. At the feature extraction stage, we firs...

Real-time EEG analysis with subject-specific spatial patterns for a brain-computer interface (BCI).

Electroencephalogram (EEG) recordings during right and left motor imagery allow one to establish a new communication channel for, e.g., patients with amyotrophic lateral sclerosis. Such an EEG-based brain-computer interface (BCI) can be used to develop a simple binary response for the control of a device. Three subjects participated in a series of on-line sessions to test if it is possible to u...

Subject position affects EEG magnitudes

EEG (electroencephalography) has been used for decades in thousands of research studies and is today a routine clinical tool despite the small magnitude of measured scalp potentials. It is widely accepted that the currents originating in the brain are strongly influenced by the high resistivity of skull bone, but it is less well known that the thin layer of CSF (cerebrospinal fluid) has perhaps...

Subject independent EEG-based BCI decoding

In the quest to make Brain Computer Interfacing (BCI) more usable, dry electrodes have emerged that get rid of the initial 30 minutes required for placing an electrode cap. Another time consuming step is the required individualized adaptation to the BCI user, which involves another 30 minutes calibration for assessing a subject’s brain signature. In this paper we aim to also remove this calibra...

Improvement of EEG classification with a subject-specific feature selection