Listening to familiar music induces continuous inhibition of alpha and low-beta power.

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 20 participants while they passively listened to 10 s 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 the local baseline, sustained low-beta power (12-16 Hz) suppression was observed in both analyses in fronto-central and left frontal electrodes after 800 ms. However, sustained alpha power (8-12 Hz) decreased in fronto-central 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 that alpha suppression reflects increased attention or arousal/engagement due to listening to familiar music; nevertheless, low-beta suppression exhibits the effect of familiarity.NEW & NOTEWORTHY This study differentiates the dynamic temporal-spectral effects during listening to 10 s of familiar music compared with unfamiliar music. This study highlights that listening to familiar music leads to continuous suppression in the alpha and low-beta bands. This suppression starts ∼800 ms after the stimulus onset.

Modulation of theta and gamma oscillations during familiarization with previously unknown music.

Repeated listening to unknown music leads to gradual familiarization with musical sequences. Passively listening to musical sequences could involve an array of dynamic neural responses in reaching familiarization with the musical excerpts. This study elucidates the dynamic brain response and its variation over time by investigating the electrophysiological changes during the familiarization with initially unknown music. Twenty subjects were asked to familiarize themselves with previously unknown 10 s classical music excerpts over three repetitions while their electroencephalogram was recorded. Dynamic spectral changes in neural oscillations are monitored by time-frequency analyses for all frequency bands (theta: 5-9 Hz, alpha: 9-13 Hz, low-beta: 13-21 Hz, high beta: 21-32 Hz, and gamma: 32-50 Hz). Time-frequency analyses reveal sustained theta event-related desynchronization (ERD) in the frontal-midline and the left pre-frontal electrodes which decreased gradually from 1st to 3rd time repetition of the same excerpts (frontal-midline: 57.90 %, left-prefrontal: 75.93 %). Similarly, sustained gamma ERD decreased in the frontal-midline and bilaterally frontal/temporal areas (frontal-midline: 61.47 %, left-frontal: 90.88 %, right-frontal: 87.74 %). During familiarization, the decrease of theta ERD is superior in the first part (1-5 s) whereas the decrease of gamma ERD is superior in the second part (5-9 s) of music excerpts. The results suggest that decreased theta ERD is associated with successfully identifying familiar sequences, whereas decreased gamma ERD is related to forming unfamiliar sequences.

Efficient Hardware Implementation of Real-Time Low-Power Movement Intention Detector System Using FFT and Adaptive Wavelet Transform.

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).

Accurate single-trial detection of movement intention made possible using adaptive wavelet transform.

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.