Biology subjects

Al-Fahad, R.

Publications and source records attributed to Al-Fahad, R..

4 recordsLinked to original sources

bioRxiv2022

Unraveling Spatial-Spectral Dynamics of Speech Categorization Speed using Convolutional Neural Network

1The process of categorizing sounds into distinct phonetic categories is known as categorical perception (CP). Response times (RTs) provide a measure of perceptual difficulty during labeling decisions (i.e., categorization). The RT is quasi-stochastic in nature due to individuality and variations in perceptual tasks. To identify the source of RT variation in CP, we have built models to decode the brain regions and frequency bands driving fast, medium and slow response decision speeds. In particular, we implemented parameter optimized convolutional neural network (CNN) to classify listeners behavioral RTs from their neural EEG data. We adopted visual interpretation of model response using Guided-GradCAM to identify spatial-spectral correlates of RT. Our framework includes (but is not limited to): (i) a data augmentation technique designed to reduce noise and control the overall variance of EEG dataset; (ii) bandpower topomaps to learn the spatial-spectral representation using CNN; (iii) large-scale Bayesian hyper-parameter optimization to find best performing CNN model; (iv) ANOVA and post-hoc analysis on Guided-GradCAM activation values to measure the effect of neural regions and frequency bands on behavioral responses. Using this framework, we observe that - {beta} (10-20 Hz) activity over left frontal, right prefrontal/frontal, and right cerebellar regions are correlated with RT variation. Our results indicate that attention, template matching, temporal prediction of acoustics, motor control and decision uncertainty are the most probable factors in RT variation.

neuroscience↗

bioRxiv2021

Micro-state-based neural decoding of speech categorization using Bayesian non-parametrics

Understanding the many-to-many mapping between patterns of functional brain connectivity and discrete behavioral responses is critical for speech-language processing. We present a microstate-based analysis of EEG recordings to characterize spatio-temporal dynamics of neural activities that underly rapid speech categorization decisions. We implemented a data driven approach using Bayesian non-parametrics to capture the mapping between EEG and the speed of listeners phoneme identification [i.e., response time (RT)] during speech labeling tasks. Based on our empirical analyses, we show task-relevant events such as resting-state, stimulus coding, auditory-perceptual object (category) formation, and response selection can be explained using patterns of micro-state dwell-time and are decodable as unique time segments during speech perception. State-dependent activities localize to a fronto-temporo-parietal circuit (superior temporal, supramarginal, inferior frontal gyri) exposing a core decision brain network (DN) underlying rapid speech categorization. Furthermore, RTs were inversely proportional to the frequency of state transitions, such that the rate of change between brain microstates was higher for trials with slower compared to faster RTs. Our findings imply that during rapid speech perception, higher uncertainty producing prolonged RTs (slower decision-making) is associated with staying in the DN longer compared lower RTs (faster decisions). We also show that listeners perceptual RTs are highly sensitive to individual differences. Our computational method opens a new avenue in segmentation and dynamic brain connectivity for modeling neuroimaging data and understanding task-related cognitive events.

neuroscience↗

bioRxiv2019

Decoding age-related changes in the spatiotemporal neural processing of speech using machine learning

Speech comprehension in noisy environments depends on complex interactions between sensory and cognitive systems. In older adults, such interactions may be affected, especially in those individuals who have more severe age-related hearing loss. Using a data-driven approach, we assessed the temporal (when in time) and spatial (where in the brain) characteristics of the cortexs speech-evoked response that distinguish older adults with or without mild hearing loss. We used source montage to model scalp-recorded during a phoneme discrimination task conducted under clear and noise-degraded conditions. We applied machine learning analyses (stability selection and control) to choose features of the speech-evoked response that are consistent over a range of model parameters and support vector machine (SVM) classification to investigate the time course and brain regions that segregate groups and speech clarity. Whole-brain data analysis revealed a classification accuracy of 82.03% [area under the curve (AUC)=81.18%; F1-score 82.00%], distinguishing groups within [~]50 ms after speech onset (i.e., as early as the P1 wave).We observed lower accuracy of 78.39% [AUC=78.74%; F1-score=79.00%] and delayed classification performance when the speech token were embedded in noise, with group segregation at 60 ms. Separate analysis using left (LH) and right hemisphere (RH) regions showed that LH speech activity was better at distinguishing hearing groups than activity measured over the RH. Moreover, stability selection analysis identified 13 brain regions (among 1428 total spatiotemporal features from 68 regions) where source activity segregated groups with >80% accuracy (clear speech); whereas 15 regions were critical for noise-degraded speech to achieve a comparable level of group segregation (76% accuracy). Our results identify two core neural networks associated with complex speech perception in older adults and confirm a larger number of neural regions, particularly in RH and frontal lobe, are active when processing degraded speech information.

bioengineering↗

bioRxiv2019

Unsupervised decoding of single-trial EEG reveals unique states of functional brain connectivity that drive rapid speech categorization decisions

Categorical perception (CP) is an inherent property of speech perception. The response time (RT) of listeners perceptual speech identification are highly sensitive to individual differences. While the neural correlates of CP have been well studied in terms of the regional contributions of the brain to behavior, functional connectivity patterns that signify individual differences in listeners speed (RT) for speech categorization is less clear. To address these questions, we applied several computational approaches to the EEG including graph mining, machine learning (i.e., support vector machine), and stability selection to investigate the unique brain states (functional neural connectivity) that predict the speed of listeners behavioral decisions. We infer that (i) the listeners perceptual speed is directly related to dynamic variations in their brain connectomics, (ii) global network assortativity and efficiency distinguished fast, medium, and slow RT, (iii) the functional network underlying speeded decisions increases in negative assortativity (i.e., became disassortative) for slower RTs, (iv) slower categorical speech decisions cause excessive use of neural resources and more aberrant information flow within the CP circuitry, (v) slower perceivers tended to utilize functional brain networks excessively (or inappropriately) whereas fast perceivers (with lower global efficiency) utilized the same neural pathways but with more restricted organization. Our results showed that neural classifiers (SVM) coupled with stability selection correctly classify behavioral RTs from functional connectivity alone with over 90% accuracy (AUC=0.9). Our results corroborate previous studies by confirming the engagement of similar temporal (STG), parietal, motor, and prefrontal regions in CP using an entirely data-driven approach.

neuroscience↗