Information-theoretic evaluation method proposed for brain-computer robot decoders

Tasha Kim and other authors formalize brain-computer robot control as a partially observable Markov decision process (POMDP) and prove that a phase transition exists: as long as any pair of targets can be distinguished by the decoder, planning regret is uniformly bounded over the entire time horizon; otherwise it grows linearly, with the bound determined by the Chernoff information Γ_min of the worst target pair. Based on this, the authors propose a decoder evaluation metric U_id based on Chernoff information, and claim that this metric can predict cumulative planning regret that accuracy cannot explain. On real EEG data (BNCI2014001 subject 5), two decoders have the same validation accuracy of 35.1%, but their held-out regret differs by a factor of 9.6. The authors claim that this work provides a directly usable diagnostic tool for sensor and decoder evaluation and selection in sequential assisted control.

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