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From Gaze to Touch: An Intracranial sEEG Bidirectional BCI for Avatar Control and Neurohaptic Feedback in Virtual Reality
Conference proceeding

From Gaze to Touch: An Intracranial sEEG Bidirectional BCI for Avatar Control and Neurohaptic Feedback in Virtual Reality

Courtnie Jean Paschall, Emmanuel Tanumihardja, Jason S. Hauptman, Jeffrey G. Ojemann, Rajesh P.N. Rao and Jeffrey Herron
International IEEE/EMBS Conference on Neural Engineering (Online), pp.628-632
11/11/2025

Abstract

Animation Brain-computer interfaces Brain-computer interfaces (BCIs) Computers Decoding Delays direct electrical stimulation (DES) Electrodes Feedback intracranial Learning (artificial intelligence) Printing sEEG Timing virtual reality (VR)
Due to protocol complexity and limited recording time, bidirectional brain-computer interfaces (BCIs) that couple neural decoding with artificial somatosensory feedback remain uncommon in clinical settings. We present a proof-of-concept intracranial, bidirectional BCI embedded in virtual reality (VR) and tested in one Epilepsy Monitoring Unit (EMU) participant. The system implements a "gaze & neural trigger" paradigm: gaze selects a virtual object, and a minimalist decoder detects high-gamma bandpower from one intracranial electrode during an overt motor cue to issue a binary grasp command. On contact, electrical stimulation (DES) of somatosensory cortex provides localized neurohaptic feedback (thumb-pressure); a catch trial without DES abolished the percept. Across 12 trials, task accuracy was 83.3% (10 / 12 correct). Mean activation delay was 4.86 \pm 5.47 ~\mathrm{s} ; the first activation required 19 s and fell to \leq 1 ~\mathrm{s} by the seventh trial, remaining \leq 1 ~\mathrm{s} thereafter. During 54 s of non-task behavior, including 25 s of intentional rest and free interaction, the decoder did not trigger-suggesting activation was governed by volitional intent rather than incidental movement. These results demonstrate a clinic-feasible, bidirectional VR-BCI coupling a lightweight single-channel decoder to VR interactions with DES feedback and motivate studies of learning and percept-driven reinforcement.

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