The ability to selectively attend to speech in the presence of other competing talkers is critical for everyday communication; yet the neural mechanisms facilitating this process are poorly understood. Here, we use electroencephalography (EEG) to study how a mixture of two speech streams is represented in the brain as subjects attend to one stream or the other. To characterize the speech-EEG relationships and how they are modulated by attention, we estimate the statistical association between each canonical EEG frequency band (delta, theta, alpha, beta, low-gamma, and high-gamma) and the envelope of each of ten different frequency bands in the input speech. Consistent with previous literature, we find that low-frequency (delta and theta) bands show greater speech-EEG coherence when the speech stream is attended compared to when it is ignored. We also find that the envelope of the low-gamma band shows a similar attention effect, a result not previously reported with EEG. This is consistent with the prevailing theory that neural dynamics in the gamma range are important for attention-dependent routing of information in cortical circuits. In addition, we also find that the greatest attention-dependent increases in speech-EEG coherence are seen in the mid-frequency acoustic bands (0.5–3 kHz) of input speech and the temporal-parietal EEG sensors. Finally, we find individual differences in the following: (1) the specific set of speech-EEG associations that are the strongest, (2) the EEG and speech features that are the most informative about attentional focus, and (3) the overall magnitude of attentional enhancement of speech-EEG coherence.

Anonymous web-based experiments are increasingly and successfully used in many domains of behavioral research. However, online studies of auditory perception, especially of psychoacoustic phenomena pertaining to low-level sensory processing, are challenging because of limited available control of the acoustics, and the unknown hearing status of participants. Here, we outline our approach to mitigate these challenges and validate our procedures by comparing web-based measurements to lab-based data on a range of classic psychoacoustic tasks. Individual tasks were created using jsPsych, an open-source javascript front-end library. Dynamic sequences of psychoacoustic tasks were implemented using Django, an open-source library for web applications, and combined with consent pages, questionnaires, and debriefing pages. Subjects were recruited via Prolific, a web-based human-subject marketplace. Guided by a meta-analysis of normative data, we developed and validated a screening procedure to select participants for (putative) normal-hearing status; this procedure combined thresholding of scores in a suprathreshold cocktail-party task with filtering based on survey responses. Headphone use was standardized by supplementing procedures from prior literature with a binaural hearing task. Individuals meeting all criteria were re-invited to complete a range of classic psychoacoustic tasks. Performance trends observed in re-invited participants were in excellent agreement with lab-based data for fundamental frequency discrimination, gap detection, sensitivity to interaural time delay and level difference, comodulation masking release, word identification, and consonant confusions. Our results suggest that web-based psychoacoustics is a viable complement to lab-based research. Source code for our infrastructure is also provided.

This study proposes a novel topology for reducing commutation torque ripple in a brushless DC motor (BLDCM) drive system using a three-level neutral-point-clamped (NPC) inverter combined with single-ended primary-inductor converter (SEPIC) converters. In the BLDCM, current ripples arise because of the influence of stator winding inductance, which generates torque ripples. The torque ripple that is generated in the commutation period prevents the use of BLDCM in high-precision servo drive systems. In this study, two-stage converters are proposed to reduce the torque ripple. The first stage consists of two SEPIC converters to obtain the desired commutation voltage according to motor speed. A dc-link voltage selection circuit is combined with the SEPIC converters to apply the optimised voltage during the commutation interval. To reduce the torque ripple further, a three-level NPC inverter is used to apply a half dc-link voltage across the motor winding and this effectively reduces the torque ripple. Experimental results show that the proposed topology is able to reduce commutation torque ripple significantly under both low-speed and high-speed operation.

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