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1. Salience of low-frequency entrainment to visual signal for classification points to predictive processing in sign language. In Proceedings of 30th Annual Computational Neuroscience Meeting: CNS*2021

   https://doi.org/10.1007/s10827-021-00801-9  

   E. Malaia, L.K. Ford (December 2021, Journal of Computational Neuroscience)

   Objectively differentiating patient mental states based on electrical activity, as opposed to overt behavior, is a fundamental neuroscience problem with medical applications, such as identifying patients in locked-in state vs. coma. Electroencephalography (EEG), which detects millisecond-level changes in brain activity across a range of frequencies, allows for assessment of external stimulus processing by the brain in a non-invasive manner. We applied machine learning methods to 26-channel EEG data of 24 fluent Deaf signers watching videos of sign language sentences (comprehension condition), and the same videos reversed in time (non-comprehension condition), to objectively separate vision-based high-level cognition states. While spectrotemporal parameters of the stimuli were identical in comprehension vs. non-comprehension conditions, the neural responses of participants varied based on their ability to linguistically decode visual data. We aimed to determine which subset of parameters (specific scalp regions or frequency ranges) would be necessary and sufficient for high classification accuracy of comprehension state. Optical flow, characterizing distribution of velocities of objects in an image, was calculated for each pixel of stimulus videos using MATLAB Vision toolbox. Coherence between optical flow in the stimulus and EEG neural response (per video, per participant) was then computed using canonical component analysis with NoiseTools toolbox. Peak correlations were extracted for each frequency for each electrode, participant, and video. A set of standard ML algorithms were applied to the entire dataset (26 channels, frequencies from .2 Hz to 12.4 Hz, binned in 1 Hz increments), with consistent out-of-sample 100% accuracy for frequencies in .2-1 Hz range for all regions, and above 80% accuracy for frequencies < 4 Hz. Sparse Optimal Scoring (SOS) was then applied to the EEG data to reduce the dimensionality of the features and improve model interpretability. SOS with elastic-net penalty resulted in out-of-sample classification accuracy of 98.89%. The sparsity pattern in the model indicated that frequencies between 0.2–4 Hz were primarily used in the classification, suggesting that underlying data may be group sparse. Further, SOS with group lasso penalty was applied to regional subsets of electrodes (anterior, posterior, left, right). All trials achieved greater than 97% out-of-sample classification accuracy. The sparsity patterns from the trials using 1 Hz bins over individual regions consistently indicated frequencies between 0.2–1 Hz were primarily used in the classification, with anterior and left regions performing the best with 98.89% and 99.17% classification accuracy, respectively. While the sparsity pattern may not be the unique optimal model for a given trial, the high classification accuracy indicates that these models have accurately identified common neural responses to visual linguistic stimuli. Cortical tracking of spectro-temporal change in the visual signal of sign language appears to rely on lower frequencies proportional to the N400/P600 time-domain evoked response potentials, indicating that visual language comprehension is grounded in predictive processing mechanisms. 

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2. Continuous and discrete decoding of overt speech with scalp electroencephalography (EEG)

   https://doi.org/10.1088/1741-2552/ad8d0a  

   Craik, Alexander; Dial, Heather; L_Contreras-Vidal, Jose (March 2025, Journal of Neural Engineering)

   Abstract Objective. Neurological disorders affecting speech production adversely impact quality of life for over 7 million individuals in the US. Traditional speech interfaces like eye-tracking devices and P300 spellers are slow and unnatural for these patients. An alternative solution, speech brain-computer interfaces (BCIs), directly decodes speech characteristics, offering a more natural communication mechanism. This research explores the feasibility of decoding speech features using non-invasive EEG.Approach. Nine neurologically intact participants were equipped with a 63-channel EEG system with additional sensors to eliminate eye artifacts. Participants read aloud sentences selected for phonetic similarity to the English language. Deep learning models, including Convolutional Neural Networks and Recurrent Neural Networks with and without attention modules, were optimized with a focus on minimizing trainable parameters and utilizing small input window sizes for real-time application. These models were employed for discrete and continuous speech decoding tasks.Main results. Statistically significant participant-independent decoding performance was achieved for discrete classes and continuous characteristics of the produced audio signal. A frequency sub-band analysis highlighted the significance of certain frequency bands (delta, theta, gamma) for decoding performance, and a perturbation analysis was used to identify crucial channels. Assessed channel selection methods did not significantly improve performance, suggesting a distributed representation of speech information encoded in the EEG signals. Leave-One-Out training demonstrated the feasibility of utilizing common speech neural correlates, reducing data collection requirements from individual participants.Significance. These findings contribute significantly to the development of EEG-enabled speech synthesis by demonstrating the feasibility of decoding both discrete and continuous speech features from EEG signals, even in the presence of EMG artifacts. By addressing the challenges of EMG interference and optimizing deep learning models for speech decoding, this study lays a strong foundation for EEG-based speech BCIs. 

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3. NeuroFlex: Feasibility of EEG-Based Motor Imagery Control of a Soft Glove for Hand Rehabilitation

   https://doi.org/10.3390/s25030610  

   Zare, Soroush; Beaber, Sameh I; Sun, Ye (February 2025, Sensors)

   Motor impairments resulting from neurological disorders, such as strokes or spinal cord injuries, often impair hand and finger mobility, restricting a person’s ability to grasp and perform fine motor tasks. Brain plasticity refers to the inherent capability of the central nervous system to functionally and structurally reorganize itself in response to stimulation, which underpins rehabilitation from brain injuries or strokes. Linking voluntary cortical activity with corresponding motor execution has been identified as effective in promoting adaptive plasticity. This study introduces NeuroFlex, a motion-intent-controlled soft robotic glove for hand rehabilitation. NeuroFlex utilizes a transformer-based deep learning (DL) architecture to decode motion intent from motor imagery (MI) EEG data and translate it into control inputs for the assistive glove. The glove’s soft, lightweight, and flexible design enables users to perform rehabilitation exercises involving fist formation and grasping movements, aligning with natural hand functions for fine motor practices. The results show that the accuracy of decoding the intent of fingers making a fist from MI EEG can reach up to 85.3%, with an average AUC of 0.88. NeuroFlex demonstrates the feasibility of detecting and assisting the patient’s attempted movements using pure thinking through a non-intrusive brain–computer interface (BCI). This EEG-based soft glove aims to enhance the effectiveness and user experience of rehabilitation protocols, providing the possibility of extending therapeutic opportunities outside clinical settings. 

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4. Uncovering Spatiotemporal Differences in Cortical Activity Corresponding to two Tasks Using Data-Driven ADASSO Algorithm

   https://doi.org/10.1109/ISBI60581.2025.10980883  

   Mai, Siwei; Haddad, Ali E; Najafizadeh, Laleh (April 2025, IEEE)

   Identifying spatiotemporal differences in brain functional dynamics corresponding to two tasks is critical for understanding how specific neural processes contribute to distinct tasks or cognitive functions. Traditional methods rely on imposing assumptions and limits on the location and timing of activities, while machine-learning-based methods generally lack offering interpretable insights. This highlights the need for new data-driven approaches to capture spatial and temporal differences in brain activity between two tasks, while also providing interpretable explanations of the neural processes underlying these differences. In this work, we formulate the problem of finding the spatial and temporal differences in the dynamics of brain function corresponding to two motor imagery (MI) tasks (left hand movement vs right hand movement) as a discriminative discrete basis problem (DDBP). We apply the data-driven asymmetric discriminative associative algorithm (ADASSO) to EEG data collected during these tasks to uncover the key functional components of the brain’s functional dynamics that differentiate between them. Results suggest that hand movements are strongly associated with high confidence activation in the motor cortex, verifying the effectiveness of the ADASSO algorithm in identifying the location and timing of cortical activities that distinguish between the two task classes. 

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5. Effective Brain Connectivity Extraction by Frequency-Domain Convergent Cross-Mapping (FDCCM) and its Application in Parkinson's Disease Classification

   https://doi.org/10.1109/TBME.2023.3250355  

   Avvaru, Sandeep; Parhi, Keshab K. (February 2023, IEEE Transactions on Biomedical Engineering)

   Objective: Inferring causal or effective connectivity between measured timeseries is crucial to understanding directed interactions in complex systems. This task is especially challenging in the brain as the underlying dynamics are not well-understood. This paper aims to introduce a novel causality measure called frequency-domain convergent cross-mapping (FDCCM) that utilizes frequency-domain dynamics through nonlinear state-space reconstruction. Method: Using synthesized chaotic timeseries, we investigate general applicability of FDCCM at different causal strengths and noise levels. We also apply our method on two resting-state Parkinson's datasets with 31 and 54 subjects, respectively. To this end, we construct causal networks, extract network features, and perform machine learning analysis to distinguish Parkinson's disease patients (PD) from age and gender-matched healthy controls (HC). Specifically, we use the FDCCM networks to compute the betweenness centrality of the network nodes, which act as features for the classification models. Result: The analysis on simulated data showed that FDCCM is resilient to additive Gaussian noise, making it suitable for real-world applications. Our proposed method also decodes scalp-EEG signals to classify the PD and HC groups with approximately 97% leave-one-subject-out cross-validation accuracy. We compared decoders from six cortical regions to find that features derived from the left temporal lobe lead to a higher classification accuracy of 84.5% compared to other regions. Moreover, when the classifier trained using FDCCM networks from one dataset was tested on an independent out-of-sample dataset, it attained an accuracy of 84%. This accuracy is significantly higher than correlational networks (45.2%) and CCM networks (54.84%). Significance: These findings suggest that our spectral-based causality measure can improve classification performance and reveal useful network biomarkers of Parkinson's disease. 

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