Steady-State movement related potentials for brain–computer interfacing

An approach for brain-computer interfacing (BCI) by analysis of steady-state movement related potentials (ssMRPs) produced during rhythmic finger movements is proposed in this paper. The neurological background of ssMRPs is briefly reviewed. Averaged ssMRPs represent the development of a lateralized rhythmic potential, and the energy of the EEG signals at the finger tapping frequency can be used for single-trial ssMRP classification. The proposed ssMRP-based BCI approach is tested using the classic Fisher's linear discriminant classifier. Moreover, the influence of the current source density transform on the performance of BCI system is investigated. The averaged correct classification rates (CCRs) as well as averaged information transfer rates (ITRs) for different sliding time windows are reported. Reliable single-trial classification rates of 88%-100% accuracy are achievable at relatively high ITRs. Furthermore, we have been able to achieve CCRs of up to 93% in classification of the ssMRPs recorded during imagined rhythmic finger movements. The merit of this approach is in the application of rhythmic cues for BCI, the relatively simple recording setup, and straightforward computations that make the real-time implementations plausible

Classifying motor imagery in presence of speech

In the near future, brain-computer interface (BCI) applications for non-disabled users will require multimodal interaction and tolerance to dynamic environment. However, this conflicts with the highly sensitive recording techniques used for BCIs, such as electroencephalography (EEG). Advanced machine learning and signal processing techniques are required to decorrelate desired brain signals from the rest. This paper proposes a signal processing pipeline and two classification methods suitable for multiclass EEG analysis. The methods were tested in an experiment on separating left/right hand imagery in presence/absence of speech. The analyses showed that the presence of speech during motor imagery did not affect the classification accuracy significantly and regardless of the presence of speech, the proposed methods were able to separate left and right hand imagery with an accuracy of 60%. The best overall accuracy achieved for the 5-class separation of all the tasks was 47% and both proposed methods performed equally well. In addition, the analysis of event-related spectral power changes revealed characteristics related to motor imagery and speech

Unimanual versus bimanual motor imagery classifiers for assistive and rehabilitative brain computer interfaces

Bimanual movements are an integral part of everyday activities and are often included in rehabilitation therapies. Yet electroencephalography (EEG) based assistive and rehabilitative brain computer interface (BCI) systems typically rely on motor imagination (MI) of one limb at the time. In this study we present a classifier which discriminates between uni-and bimanual MI. Ten able bodied participants took part in cue based motor execution (ME) and MI tasks of the left (L), right (R) and both (B) hands. A 32 channel EEG was recorded. Three linear discriminant analysis classifiers, based on MI of L-B, B-R and B--L hands were created, with features based on wide band Common Spatial Patterns (CSP) 8-30 Hz, and band specifics Common Spatial Patterns (CSPb). Event related desynchronization (ERD) was significantly stronger during bimanual compared to unimanual ME on both hemispheres. Bimanual MI resulted in bilateral parietally shifted ERD of similar intensity to unimanual MI. The average classification accuracy for CSP and CSPb was comparable for L-R task (73±9% and 75±10% respectively) and for L-B task (73±11% and 70±9% respectively). However, for R-B task (67±3% and 72±6% respectively) it was significantly higher for CSPb (p=0.0351). Six participants whose L-R classification accuracy exceeded 70% were included in an on-line task a week later, using the unmodified offline CSPb classifier, achieving 69±3% and 66±3% accuracy for the L-R and R-B tasks respectively. Combined uni and bimanual BCI could be used for restoration of motor function of highly disabled patents and for motor rehabilitation of patients with motor deficits