Investigating the Brain Activity Correlates of Humming Bee Sound during Bhramari Pranayama.

Context: Previous research has shown the vast benefits associated with BhP. However, the dynamics of cortical activity in connection with Bhramari sound have not been investigated yet. Aim: To investigate the cortical activity in connection with Bhramari sound. Settings and Design: Humming sound was analyzed with a custom-made nasal device consisting of MAX4466 sensor time synchronized with the EEG setup. We anticipated that the modulation of cortical activity with the humming sound (either of long or short durations) leaves its effects after the Pranayama, which helps to understand the positive impacts of BhP. Methods and Material: 30 participants were instructed to perform the BhP for a period of 90 seconds. We proposed to investigate the cortical correlates before, during, and after the BhP through EEG. A custom-made nasal device consisting of MAX4466 sensor time synchronized with the EEG setup was used for analyzing the humming sound. Statistical Analysis Used: A paired t-test (P < 0.05) with a Bonferroni correction is carried out to explore the statistically significant difference in power spectral density (PSD) values. Results: Results show that the relative spectral power in theta band for short humming durations (less than or equal to 9 seconds) was similar on the frontal cortex during and after the Pranayama practice (P > 0.05) in most of the subjects. Conclusions: In conclusion, for the immediate positive effects of BhP, the humming duration should be kept less than or equal to 9 seconds. A wearable sound recording system can be developed in the future as a feedback system that provides biofeedback to the user so that a constant humming duration can be maintained.

Feature selection using regularized neighbourhood component analysis to enhance the classification performance of motor imagery signals.

In motor imagery (MI) based brain-computer interface (BCI) signal analysis, mu and beta rhythms of electroencephalograms (EEGs) are widely investigated due to their high temporal resolution and capability to define the different movement-related mental tasks separately. However, due to the high dimensions and subject-specific behaviour of EEG features, there is a need for a suitable feature selection algorithm that can select the optimal features to give the best classification performance along with increased computational efficiency. The present study proposes a feature selection algorithm based on neighbourhood component analysis (NCA) with modification of the regularization parameter. In the experiment, time, frequency, and phase features of the EEG are extracted using a dual-tree complex wavelet transform (DTCWT). Afterwards, the proposed algorithm selects the most significant EEG features, and using these selected features, a support vector machine (SVM) classifier performs the classification of MI signals. The proposed algorithm has been validated experimentally on two public BCI datasets (BCI Competition II Dataset III and BCI Competition IV Dataset 2b). The classification performance of the algorithm is quantified by the average accuracy and kappa coefficient, whose values are 80.7% and 0.615 respectively. The performance of the proposed algorithm is compared with standard feature selection methods based on Genetic Algorithm (GA), Principal Component Analysis (PCA), and ReliefF and performs better than these methods. Further, the proposed algorithm selects the lowest number of features and results in increased computational efficiency, which makes it a promising feature selection tool for an MI-based BCI system.