Beta-band power classification of go/no-go arm-reaching responses in the human hippocampus

TL;DR

This study shows that hippocampal beta-band activity can distinguish between movement execution and inhibition during arm-reaching tasks in humans.

Contribution

The novel contribution is demonstrating effective classification of Go/No-go trials using hippocampal beta-band power with PCA and a Diagonal-Quadratic model.

Findings

The Diagonal-Quadratic model achieved a median error rate of 9.91% in classifying Go/No-go trials.

PCA captured up to 81.25% variance in beta-band power across participants.

Hippocampal beta-band modulation is significant for motor control and potential brain-computer interface applications.

Abstract

Objective. Can we classify movement execution and inhibition from hippocampal oscillations during arm-reaching tasks? Traditionally associated with memory encoding, spatial navigation, and motor sequence consolidation, the hippocampus has come under scrutiny for its potential role in movement processing. Stereotactic electroencephalography (SEEG) has provided a unique opportunity to study the neurophysiology of the human hippocampus during motor tasks. In this study, we assess the accuracy of discriminant functions, in combination with principal component analysis (PCA), in classifying between ‘Go’ and ‘No-go’ trials in a Go/No-go arm-reaching task. Approach. Our approach centers on capturing the modulation of beta-band (13–30 Hz) power from multiple SEEG contacts in the hippocampus and minimizing the dimensional complexity of channels and frequency bins. This study utilizes SEEG data from the human hippocampus of 10 participants diagnosed with epilepsy. Spectral power was computed during a ‘center-out’ Go/No-go arm-reaching task, where participants reached or withheld their hand based on a colored cue. PCA was used to reduce data dimension and isolate the highest-variance components within the beta band. The Silhouette score was employed to measure the quality of clustering between ‘Go’ and ‘No-go’ trials. The accuracy of five different discriminant functions was evaluated using cross-validation. Main results. The Diagonal-Quadratic model performed best of the 5 classification models, exhibiting the lowest error rate in all participants (median: 9.91%, average: 14.67%). PCA showed that the first two principal components collectively accounted for 54.83% of the total variance explained on average across all participants, ranging from 36.92% to 81.25% among participants. Significance. This study shows that PCA paired with a Diagonal-Quadratic model can be an effective method for classifying between Go/No-go trials from beta-band power in the hippocampus during arm-reaching responses. This emphasizes the significance of hippocampal beta-power modulation in motor control, unveiling its potential implications for brain–computer interface applications.

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