Temporal Generalization in fNIRS-Based Autism Classification: A Cross-Time-Window Transfer Benchmark
The episode discusses a paper benchmarking temporal generalization in fNIRS-based autism classification. The hosts explain how timing differences between subjects cause accuracy to drop when models are trained and tested on different time windows of brain activity. Key findings show that subject-specific calibration, especially using small amounts of target data, is crucial for achieving high accuracy.