Enhancing neural signal quality: a spike-denoising model with BiLSTM and attention mechanism

Neural signal spikes are recorded by microelectrode technology for specific applications. Nevertheless, spikes frequently encounter significant contamination from several noise sources, making efficient denoising quite challenging. Hence, this study presents a spike-denoising model with a bidirectional long short-term memory and attention mechanism combined with a shallow autoencoder to enhance the signal quality. To assess the effectiveness of the proposed method, various types of synthetic data, such as simulated white noise, correlated noise, colored noise and integrated noise, are used to show the performance. At very high noise levels, the proposed method maintains a high signal-to-noise ratio above 27 dB and the average Pearson 0.91. And the performance metrics of spike detection outperforms the traditional signal processing methods and the partial deep learning methods. Ultimately, the proposed method is used to process the real-world C57 fetal rat neural signals, which can recover a substantial amount of spikes from the obscured noise, showing the proposed method can effectively enhance the quality of neural signals damaged by noise.