Department Publications

In Electroencephalography (EEG) classifiers, processing of EEG signals consists of extracting features from EEG signal and then processing those features to classify the signals. As of today, in most EEG processing systems, the classification part is done on software platform even though the feature extraction is done on hardware. Hence this require an operating system which limits the development of portable medical devices. This paper presents the design and implementation of feature classification in EEG signal processing system on Field Programmable Gate Array (FPGA) hardware platform. The classification is done with two-class Linear Discriminant Analysis (LDA), based on the features extracted using Discrete Wavelet Transform (DWT), for EEG signals obtained through PhysioNet website. The final design has minimum resource utilization, hence is able to implement on Basys 3 Artix-7 FPGA Board …

Eelbrain - ADS Now on home page ads icon ads Enable full ADS view ADS Eelbrain Brodbeck, Christian ; Das, Proloy ; Brooks, Teon L ; Reddigari, Samir ; jpkulasingham Abstract No description provided. Publication: Zenodo Pub Date: DOI: 10.5281/zenodo.7951251 Bibcode: 2023zndo...7951251B full text sources Zenodo | Related Materials (2) Version 0.36 Version v0.32 © The SAO Astrophysics Data System adshelp[at]cfa.harvard.edu The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative Agreement 80NSSC21M0056 Smithsonian logo Harvard Center for Astrophysics logo NASA logo *The material contained in this document is based upon work supported by a National Aeronautics and Space Administration (NASA) grant or cooperative agreement. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author and do not …

Neural processing along the ascending auditory pathway is often associated with a progressive reduction in characteristic processing rates. For instance, the well-known frequency-following response (FFR) of the auditory midbrain, as measured with electroencephalography (EEG), is dominated by frequencies from~ 100 Hz to several hundred Hz, phase-locking to the stimulus waveform at those frequencies. In contrast, cortical responses, whether measured by EEG or magnetoencephalography (MEG), are typically characterized by frequencies of a few Hz to a few tens of Hz, time-locking to acoustic envelope features. In this study we investigated a crossover, cortically generated responses time-locked to continuous speech features at FFR-like rates. Using MEG, we analyzed high-frequency responses (70-300 Hz) to continuous speech using neural source-localized reverse correlation and its corresponding temporal response functions (TRFs). Continuous speech stimuli were presented to 40 subjects (17 younger, 23 older adults) with clinically normal hearing and their MEG responses were analyzed in the 70-300 Hz band. Consistent with the insensitivity of MEG to many subcortical structures, the spatiotemporal profile of these response components indicated a purely cortical origin with~ 40 ms peak latency and a right hemisphere bias. TRF analysis was performed using two separate aspects of the speech stimuli: a) the 70-300 Hz band of the speech waveform itself, and b) the 70-300 Hz temporal modulations in the high frequency envelope (300-4000 Hz) of the speech stimulus. The response was dominantly driven by the high frequency …