Batch decode a neural signal file

This lesson looks at a much more unusual capability, the brain-computer interface.

BCI models interpret neural signals into something the rest of the SDK can consume. The pattern is the same as every other capability: load a model, hand it input, read back the structured result. The inputs and outputs are unusual, but the workflow isn't.

The model that drives this chapter is BCI_WINDOWED. It bundles a Whisper-style decoder with a brain-computer-interface projection layer that turns a raw neural-signal .bin file into the same audio-token space Whisper expects. The output is the same kind of timed transcript you'd get from a microphone recording.

The BCI model has TWO configs in one modelConfig: whisperConfig for the decoder, bciConfig for the neural data. The two-config block would look like the following:

const modelId = await loadModel({
  modelSrc: BCI_WINDOWED,
  modelConfig: {
    whisperConfig: {
      language: "en",
      n_threads: 4,
      temperature: 0.0,
    },
    bciConfig: {
      day_idx: 1,
    },
  },
});

bciTranscribe returns an array of segments, each with timestamp, id, append flag, and decoded text. You would call it like so:

const segments = await bciTranscribe({
  modelId,
  neuralData: neuralFilePath,
  metadata: true,
});

Each segment carries a timestamp and a metadata block. We iterate and log the text, the start/end in seconds, the id, and the append flag:

for (const segment of segments) {
  const start = (segment.startMs / 1000).toFixed(2);
  const end = (segment.endMs / 1000).toFixed(2);
  console.log(
    `  [${start}s → ${end}s] (id=${segment.id}, append=${segment.append}) ${segment.text}`,
  );
}

The bciConfig.day_idx field picks which day-specific projection matrices the model uses. Set it to match the recording session your neural file came from. The example default is day 1.

The append field on each segment tells you whether the new text continues the previous segment or starts a new one. A live UI uses it to decide between overwriting the last caption and appending.

Note: the Whisper half of the BCI pipeline takes the same whisperConfig knobs the standalone Whisper model does (language, n_threads, temperature). For batch decode, n_threads: 4 and temperature: 0.0 are sensible defaults.