Single-trial decoding of intended eye movement goals from lateral prefrontal cortex neural ensembles
Bibliographic record
Abstract
Neurons in the lateral prefrontal cortex (LPFC) encode sensory and cognitive signals, as well as commands for goal-directed actions. Therefore, the LPFC might be a good signal source for a goal-selection brain-computer interface (BCI) that decodes the intended goal of a motor action previous to its execution. As a first step in the development of a goal-selection BCI, we set out to determine if we could decode simple behavioral intentions to direct gaze to eight different locations in space from single-trial LPFC neural activity. We recorded neuronal spiking activity from microelectrode arrays implanted in area 8A of the LPFC of two adult macaques while they made visually guided saccades to one of eight targets in a center-out task. Neuronal activity encoded target location immediately after target presentation, during a delay epoch, during the execution of the saccade, and every combination thereof. Many (40%) of the neurons that encoded target location during multiple epochs preferred different locations during different epochs. Despite heterogeneous and dynamic responses, the neuronal feature set that best predicted target location was the averaged firing rates from the entire trial and it was best classified using linear discriminant analysis (63.6-96.9% in 12 sessions, mean 80.3%; information transfer rate: 21-59, mean 32.8 bits/min). Our results demonstrate that it is possible to decode intended saccade target location from single-trial LPFC activity and suggest that the LPFC is a suitable signal source for a goal-selection cognitive BCI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".