The Berlin brain-computer interface: EEG-based communication without subject training

Abstract

The Berlin Brain-Computer Interface (BBCI) project develops a noninvasive BCI system whose key features are 1) the use of well-established motor competences as control paradigms, 2) high-dimensional features from 128-channel electroencephalogram (EEG), and 3) advanced machine learning techniques. As reported earlier, our experiments demonstrate that very high information transfer rates can be achieved using the readiness potential (RP) when predicting the laterality of upcoming left- versus right-hand movements in healthy subjects. A more recent study showed that the RP similarily accompanies phantom movements in arm amputees, but the signal strength decreases with longer loss of the limb. In a complementary approach, oscillatory features are used to discriminate imagined movements (left hand versus right hand versus foot). In a recent feedback study with six healthy subjects with no or very little experience with BCI control, three subjects achieved an information transfer rate above 35 bits per minute (bpm), and further two subjects above 24 and 15 bpm, while one subject could not achieve any BCI control. These results are encouraging for an EEG-based BCI system in untrained subjects that is independent of peripheral nervous system activity and does not rely on evoked potentials even when compared to results with very well-trained subjects operating other BCI systems.

Advanced Neurotechnologies for Chronic Neural Interfaces: New Horizons and Clinical Opportunities

“…Noninvasive BCIs use electroencephalographic (EEG) activity recorded from the scalp (Farwell and Donchin, 1988;Wolpaw et al, 1991;Sutter, 1992;McFarland et al, 1993McFarland et al, , 2008Pfurtscheller et al, 1993Pfurtscheller et al, , 2000McFarland, 1994, 2004;Birbaumer et al, 1999;Millán et al, 2004;Kübler et al, 2005;Blankertz et al, 2006;Vaughan et al, 2006;Müller et al, 2008). Although EEG-based BCIs support higher performance than often assumed, the acquisition of high levels of brain-based control typically requires extensive user training, and BCI performance can also be variable.…”

Signal Processing and Classification Approaches for Brain-Computer Interface

“…Frequency representation (FR) features have been widely used in signal processing because of their ease of application, computational speed and direct interpretation of the results Blankertz et al, 2006). Specifically, about one-third of BCI designs have used power-spectral density (PSD) features .…”

Advances in Brain, Vision, and Artificial Intelligence

“…Spontaneous BCIs are based on the analysis of EEG phenomena associated with various aspects of brain function related to mental tasks carried out by the subject at his/her own will. Such a kind of BCI can exploit two kinds of spontaneous, or endogenous, brain signals, namely slow potential shifts [10] or variations of rhythmic activity [6], [8], [11], [12], [13], [14]. We will focus on the latter that are the most common.…”