Decode, Then Average: Real-Time Visual EEG Decoding from Single Trials

Decoding perceived images from non-invasive electroencephalography (EEG) has progressed rapidly in recent years. This progress relies on averaging, a practice inherited from decades of neurophysiology. An individual EEG trial is dominated by noise, so decoders are trained on averages of repeated trials of each image, and at test time every available trial of a test image is averaged into one clean query that is decoded once. We argue that the single trial, not the average, should be the default operating point. We first build a decoder for this regime, with every design choice, from training data to hyperparameters, made for single trials. On THINGS-EEG2, the standard benchmark for this task, where a decoder must identify which of 200 candidate images evoked a response, it attains $27.39\%$ top-1 accuracy from one trial, more than twice the single-trial accuracy of the strongest published method. From this base, the decoder scores each trial separately and averages the resulting decisions, a readout we call *decode-then-average*, in contrast to the standard *average-then-decode*. Aggregating decisions rather than signals offers three advantages. (1) **Data scale**: training on trials rather than on their averages preserves the training set at its full recorded size. (2) **EEG response timing**: averaging superimposes responses whose latencies differ from trial to trial, whereas per-trial decisions preserve each response's own timing. (3) **Real-time decoding**: the number of trials becomes a decision made during inference. A patience stop during real-time decoding, halting presentation once the answer stops changing, matches the 80-trial accuracy of the strongest published method from $2.6$ trials per image, a $96.8\%$ reduction in presentations. The averaged decisions reach $94.50\%$ with all 80 trials and exceed the accuracy of every published method, under either readout, at every trial count. Real-time decoding also exposes a new property of the stimuli: each image has a stable *decodability*, from under four trials for the most decodable to nearly twice as many for the least. Decodability reflects how crowded an image's neighborhood is among the 200 candidates, not its pixels, and a new subject's per-image trial counts can be predicted in advance. Single-trial EEG decoding is therefore more tractable than existing results indicate, and treating it as the default turns decoding into a real-time process with a measurable, predictable cost.