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.

运动脑机接口（BMI）直接将大脑与人工执行器连接起来，并有可能减轻因神经损伤或疾病引起的严重身体瘫痪。大多数 BMI 系统都包含一个解码器，用于分析神经尖峰计数以推断运动意图。然而，许多经典的 BMI 解码器 (1) 未能利用尖峰序列的时间模式，可能在很长的时间范围内； (2) 不足以在高时间分辨率下实现良好的 BMI 性能，因为违反了基于尖峰计数的解码器的基本高斯假设。在这里，我们提出了一种新的统计特征，它表示具有更丰富描述的尖峰事件的时间模式或时间代码——小波平均系数（WAC）——用作解码器输入而不是尖峰计数。我们通过使用 WAC 特征和滑动窗口方法构建了一个小波解码器框架，并将所得解码器与经典解码器（维纳和卡尔曼家族）和使用尖峰计数特征的新的基于深度学习的解码器（长短期记忆）进行了比较。我们发现滑动窗口方法提高了解码时间分辨率，并且使用 WAC 特征比使用尖峰计数特征显着提高了解码性能。