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 shape 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, and the confidence:

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. Day 1 is the example default.

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.

Put it to the test

1. Call loadModel with modelSrc: BCI_WINDOWED, a whisperConfig block (language: "en", n_threads: 4, temperature: 0.0), and bciConfig: { day_idx: 1 }.
2. Call await bciTranscribe({ modelId, neuralData: neuralFilePath, metadata: true }).
3. Iterate the segments with a for loop and console.log each with its [start → end] timestamp and id and append fields.