About this trial
The clinical investigation evaluates whether speech can be decoded from neural activity recorded with cortical surface electrocorticography (ECoG) electrodes during awake brain tumor surgery. Neural signals and voice recordings are collected while patients perform a picture naming task as part of standard intraoperative mapping. The study assesses the ability of machine learning algorithms to predict the named item from intraoperative electrophysiological recordings.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Patients 18 years of age or older.
Patients with a brain tumor requiring surgery under "awake" conditions with intraoperative placement of ECoG electrodes at Lariboisière Hospital.
Disqualifiers
Patients with severe cognitive impairments that would prevent understanding and performing the image-naming task,
Patients with major psychiatric disorders that could affect their ability to participate in the research,
Patients with inoperable tumors,
Patients with tumor locations incompatible with safe or effective placement of ECoG electrodes,
Trial design
Single group
Treatments tested in this trial
Placement of high-density electrodes on cortical surface during awake surgery
Procedure/SurgeryINBRAIN Graphene Cortical Interface (Non-CE-marked Class III )high-density graphene ECoG grid placed on cortical surface for up to 2 hours during awake surgery. Recording only, no stimulation.
Placement of INBRAIN Graphene Cortical Interface on cortical surface
Procedure/SurgeryINBRAIN Graphene Cortical Interface (Non-CE-marked Class III )high-density graphene ECoG grid placed on cortical surface for up to 2 hours during awake brain surgery. Recording only, no stimulation.
Placement of low-density electodes during awake brain surgery
Procedure/SurgeryDIXI C10-12BIOM and WISE WCS00-4S10-000 (CE-marked Class III) low-density ECoG strips placed on cortical surface for up to 2 hours during awake surgery. Recording only, no stimulation.
Treatment groups
Trial outcomes
Primary outcomes
Primary outcome Measure
The primary metric for evaluating the success of the project is the accuracy of the machine learning algorithms in decoding neural activity to predict the named item by the patient. Achieving an Area Under the Curve (AUC) value of greater than 0.9 indicates high predictive performance.
Secondary outcomes
Secondary Outcome Measure
The evaluation criterion is to identify axono-cortical evoked potentials (ACEPs) with a signal- to-noise ratio \> 1. The parameters consist of all recordings made by surface electrodes during axonal electrical stimulation at 1 Hz. On each channel, averaging will be performed, synchronized with the 1 Hz stimulation.
Sponsors and contacts
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