Abstract
Motor brain machine interfaces (BMIs) directly link the brain to artificial actuators and have the potential to mitigate severe body paralysis caused by neurological injury or disease. Most BMI systems involve a decoder that analyzes neural spike counts to infer movement intent. However, many classical BMI decoders (1) fail to take advantage of temporal patterns of spike trains, possibly over long time horizons; (2) are insufficient to achieve good BMI performance at high temporal resolution, as the underlying Gaussian assumption of decoders based on spike counts is violated. Here, we propose a new statistical feature that represents temporal patterns or temporal codes of spike events with richer description—wavelet average coefficients (WAC)—to be used as decoder input instead of spike counts. We constructed a wavelet decoder framework by using WAC features with a sliding-window approach, and compared the resulting decoder against classical decoders (Wiener and Kalman family) and new deep learning based decoders ( Long Short-Term Memory) using spike count features. We found that the sliding-window approach boosts decoding temporal resolution, and using WAC features significantly improves decoding performance over using spike count features.
Highlights
Motor brain machine interfaces (BMIs) directly link the brain to artificial actuators and have the potential to mitigate severe body paralysis caused by neurological injury or disease
Through various experiments, we found that BMI decoders can better decode kinematics from trend features than from spike counts as trend features encode temporal patterns of spike events
To show that our wavelet average coefficients (WAC) is a richer feature compared to spike counts in different decoding platforms (from simple regressions model (e.g., Wiener or Kalman Filter) to advanced deep networks (LSTM)), we demonstrated that the decoding performance of a Long Short-Term Memory (LSTM) decoder using WAC is better than that of LSTM decoder using spike counts in 5ms high temporal resolution (Fig. 7)
Summary
Motor brain machine interfaces (BMIs) directly link the brain to artificial actuators and have the potential to mitigate severe body paralysis caused by neurological injury or disease. Classical BMIs (e.g., Wiener and Kalman filter ) assumes the spike counts within each bin are Gaussian and updates every 50-100 ms This bin width usually provides good temporal resolution and a sufficient amount of neuronal data needed for accurate decoding, but the Gaussian assumption can sometimes be violated. Several recent publications[9,10,11,12,13,14,15] have argued that even the temporal resolution of 50-100 ms is insufficient for high BMI performance, and a resolution of 5 ms is preferable Decoding at such a high temporal resolution would severely decrease the decoding performance, as spike counts in 5 ms bins severely violate the classical filter’s approximately Gaussian assumption. Temporal c odes[20,22,23,24,25,26,27] employ those features of the spiking activity that cannot be described by the firing rate (e.g., time to first spike, phase of firing, etc.) alone
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