a b s t r a c t a r t i c l e i n f oIn the current study we use electroencephalography (EEG) to detect heard music from the brain signal, hypothesizing that the time structure in music makes it especially suitable for decoding perception from EEG signals. While excluding music with vocals, we classified the perception of seven different musical fragments of about three seconds, both individually and cross-participants, using only time domain information (the event-related potential, ERP). The best individual results are 70% correct in a seven-class problem while using single trials, and when using multiple trials we achieve 100% correct after six presentations of the stimulus. When classifying across participants, a maximum rate of 53% was reached, supporting a general representation of each musical fragment over participants. While for some music stimuli the amplitude envelope correlated well with the ERP, this was not true for all stimuli. Aspects of the stimulus that may contribute to the differences between the EEG responses to the pieces of music are discussed.

Combining electrophysiological and hemodynamic features is a novel approach for improving current performance of brain switches based on sensorimotor rhythms (SMR). This study was conducted with a dual purpose: to test the feasibility of using a combined electroencephalogram/functional near-infrared spectroscopy (EEG-fNIRS) SMR-based brain switch in patients with tetraplegia, and to examine the performance difference between motor imagery and motor attempt for this user group. A general improvement was found when using both EEG and fNIRS features for classification as compared to using the single-modality EEG classifier, with average classification rates of 79% for attempted movement and 70% for imagined movement. For the control group, rates of 87% and 79% were obtained, respectively, where the "attempted movement" condition was replaced with "actual movement." A combined EEG-fNIRS system might be especially beneficial for users who lack sufficient control of current EEG-based brain switches. The average classification performance in the patient group for attempted movement was significantly higher than for imagined movement using the EEG-only as well as the combined classifier, arguing for the case of a paradigm shift in current brain switch research.

It is an implicit assumption in the field of brain-computer interfacing (BCI) that BCIs can be satisfactorily used to access augmentative and alternative communication (AAC) methods by people with severe physical disabilities. A one-day workshop and focus group interview was held to investigate this assumption. Rehabilitation professionals (N = 28) were asked to critically assess current BCI technology, recommend design requirements and identify target users. The individual answers were analyzed using the theoretical framework of grounded theory. None of the participants expressed a perception of added value of current BCIs over existing alternatives. A major criticism (and requirement) was that the usability of BCI systems should significantly improve. Target users are only those who can hardly or not at all use alternative access technologies. However, such persons often have concurrent physical, sensory, and cognitive problems, which could complicate BCI use. If successful BCI use continues to require a user to sit motionlessly and have intact cognition, then -as previously implicitly assumed -people in the locked-in state (resulting from late-stage amyotrophic lateral sclerosis, multiple sclerosis, spinal muscular atrophy type II or classic or total locked-in syndrome) and people with high spinal cord injury (C1/C2) could be target users.

Motor-impaired individuals such as tetraplegics could benefit from Brain-Computer Interfaces with an intuitive control mechanism, for instance for the control of a neuroprosthesis. Whereas BCI studies in healthy users commonly focus on motor imagery, for the eventual target users, namely patients, attempted movements could potentially be a more promising alternative. In the current study, EEG frequency information was used for classification of both imagined and attempted movements in tetraplegics. Although overall classification rates were considerably lower for tetraplegics than for the control group, both imagined and attempted movement were detectable. Classification rates were significantly higher for the attempted movement condition, with a mean rate of 77%. These results suggest that attempted movement is an appropriate task for BCI control in long-term paralysis patients.