Computers in Biology and Medicine

A perspective review published in Computers in biology and medicine examines EEG non-stationarity across sessions, people, and recording conditions. It asks whether mechanisms that help the brain maintain functional stability can improve the robustness of brain-computer interface models. The review covers synaptic plasticity, homeostatic regulation, neural oscillations, and spiking representations, comparing bio-inspired approaches with conventional machine learning and transfer learning. It also considers hybrid designs that combine biologically grounded mechanisms with artificial neural networks. The author proposes operational definitions for bio-inspired, bio-plausible, and bio-realistic modeling, together with a minimum specification for continual EEG benchmarks. Because direct EEG evidence remains limited for several proposed mechanisms, the review stresses the need to distinguish empirical findings from hypotheses and future research directions.

A study published in Computers in biology and medicine introduces LEGEND, a neural-decoding architecture that jointly models cortical EEG, spinal ESG, and peripheral-muscle EMG for a neural bypass in spinal cord injury rehabilitation. The model encodes the three signal modalities in Lorentz hyperbolic space, connects 51 channel nodes through a signed tri-layer phase-locking-value graph, and refines the representation with graph attention. Under strict leave-one-subject-out evaluation on the Steele dataset, LEGEND achieved 56.51%±12.27% accuracy, 23.4 percentage points above EEGNet. The researchers argue that hyperbolic representations can capture complex relationships across the motor hierarchy, providing a computational foundation for rehabilitation decoding that links brain, spinal, and muscle activity.

A study published in Computers in biology and medicine proposes dynamic wavelet-basis selection to improve non-invasive EEG imagined-speech classification. For each EEG epoch, the method minimizes wavelet entropy to select an informative basis and then injects Gaussian noise into the corresponding coefficients. A convolutional neural network with channel-wise excitation classifies the augmented signals. The dataset contains 32 channels, 8 stimuli, and recordings from 10 participants. The words-vowels combination reached a highest classification accuracy of 98% with a Cohen's kappa of 0.95, although performance was lower for the full class set. The researchers report that the method outperformed conventional augmentation strategies and static wavelet approaches, offering an adaptive way to address noise and non-stationarity in EEG decoding.

A study published in Computers in biology and medicine applies cross-frequency bispectral analysis to nonlinear EEG activity during the planning and execution of natural reach-to-grasp movements. The researchers extracted magnitude- and phase-based features from complex bicoherence matrices and assessed them through classification, permutation-based feature selection, and within-subject statistics. Execution showed stronger nonlinear coupling than planning, led mainly by beta- and gamma-driven interactions. Decoding precision versus power grasps performed similarly across the two stages, suggesting that grasp-type representations emerge during planning and persist into execution. Compared with conventional analytical baselines, bispectral features provided consistent advantages for grasp-type discrimination and multiclass classification. The findings offer a new set of motor-decoding features for future brain-computer interface and neuroprosthetic research.