Abstract. Brain Computer Interface (BCI) systems, which are based on motor imagery, enable humans to command arti cial peripherals by merely thinking about the task. There is a tremendous interest in implementing BCIs on portable platforms, such as Field Programmable Gate Arrays (FPGAs) due to their low-cost, low-power and portability characteristics. This article presents the design and implementation of a Brain Computer Interface (BCI) system based on motor imagery on a Virtex-6 FPGA. In order to design an accurate algorithm, the proposed method avails statistical learning methods such as Mutual Information (MI), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM). It also uses Separable Common Spatio Spectral Pattern (SCSSP) method in order to extract features. Simulation results prove achieved performances of 73.54% for BCI competition III-dataset V, 67.2% for BCI competition IV-dataset 2a with all four classes, 80.55% for BCI competition IV-dataset 2a with the rst two classes, and 81.9% for captured signals. Moreover, the nal reported hardware resources determine its e ciency as a result of using retiming and folding techniques from the VLSI architecture' perspective. The complete proposed BCI system achieves not only excellent recognition accuracy, but also remarkable implementation e ciency in terms of portability, power, time, and cost.

The brain-computer interfacing (BCI), a platform to extract features and classify different motor movement tasks from noisy and highly correlated electroencephalogram signals, is limited mostly by the complex and power-hungry algorithms. Among different techniques recently devised to tackle this issue, real-time onset detection, due to its negligible delay and minimal power overhead, is the most efficient one. Here, we propose a novel algorithm that outperforms the state-of-the-art design by sixfold in terms of speed, without sacrificing the accuracy for a real-time, hand movement intention detection based on the adaptive wavelet transform with only 1 s detection delay and maximum sensitivity of 88% and selectivity of 78% (only 7% loss of sensitivity).

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The outlook of brain-computer interfacing (BCI) is very bright. The real-time, accurate detection of a motor movement task is critical in BCI systems. The poor signal-to-noise-ratio (SNR) of EEG signals and the ambiguity of noise generator sources in brain renders this task quite challenging. In this paper, we demonstrate a novel algorithm for precise detection of the onset of a motor movement through identification of event-related-desynchronization (ERD) patterns. Using an adaptive matched filter technique implemented based on an optimized continues Wavelet transform by selecting an appropriate basis, we can detect single-trial ERDs. Moreover, we use a maximum-likelihood (ML), electrooculography (EOG) artifact removal method to remove eye-related artifacts to significantly improve the detection performance. We have applied this technique to our locally recorded Emotiv(®) data set of 6 healthy subjects, where an average detection selectivity of 85 ± 6% and sensitivity of 88 ± 7.7% is achieved with a temporal precision in the range of -1250 to 367 ms in onset detections of single-trials.

Frequent listening to unfamiliar music excerpts converges functional connectivity in the brain as music becomes familiar and memorable. Nonetheless, previous neuroimaging and electroencephalography (EEG) studies have yet to determine where and when these connections arise in the brain during familiarization. This study investigates electrophysiological changes in functional connectivity recorded by EEG from twenty participants’ brains during self-assessment familiarization with initially unknown classical music excerpts via three times passive listening. Connectivity between all pairwise combinations of EEG electrodes is evaluated across all repetitions via repeated measures ANOVA and between every two repetitions of listening to unknown music with the weighted phase lag index (WPLI) method in different frequency bins and bands. The results of the WPLI method indicate an increased coupling during gradual familiarization between the frontal and parietal areas in the theta band, especially at 7 Hz. In addition, the increased functional coupling is discovered during music familiarization between the frontal and temporal areas at the low-alpha band. Moreover, during listening to music, whether familiar or unfamiliar, robust functional connectivity between the frontal and parietal areas occurs in the alpha band compared to other bands, regardless of the number of repetitions and familiarization. Overall, this study revealed that repeated listening to music activates specific functional connectivity in the brain: familiarization increases bi-spectral functional connections between the frontal and parietal areas at the theta band and between the frontal and temporal areas at the low-alpha band.

How the brain responds temporally and spectrally when we listen to familiar versus unfamiliar musical sequences remains unclear. This study uses EEG techniques to investigate the continuous electrophysiological changes in the human brain during passive listening to familiar and unfamiliar musical excerpts. EEG activity was recorded in twenty participants while passively listening to 10 seconds of classical music, and they were then asked to indicate their self-assessment of familiarity. We analyzed the EEG data in two manners: familiarity based on the within-subject design, i.e., averaging trials for each condition and participant, and familiarity based on the same music excerpt, i.e., averaging trials for each condition and music excerpt. By comparing the familiar condition with the unfamiliar condition and local baseline, sustained low-beta power (12-16 Hz) suppression was observed in both analyses in frontocentral and left frontal electrodes after 800 ms. However, sustained alpha power (8-12 Hz) decreased in frontocentral and posterior electrodes after 850 ms only in the first type of analysis. Our study indicates that listening to familiar music elicits a late sustained spectral response (inhibition of alpha/low-beta power from 800 ms to 10 s). Moreover, the results showed alpha suppression reflects increased attention or arousal/engagement due to listening to familiar music; nevertheless, low-beta suppression exhibits the effect of familiarity.