Abstract
Objective. There has recently been an increasing interest in local field potential (LFP) for brain-machine interface (BMI) applications due to its desirable properties (signal stability and low bandwidth). LFP is typically recorded with respect to a single unipolar reference which is susceptible to common noise. Several referencing schemes have been proposed to eliminate the common noise, such as bipolar reference, current source density (CSD), and common average reference (CAR). However, to date, there have not been any studies to investigate the impact of these referencing schemes on decoding performance of LFP-based BMIs. Approach. To address this issue, we comprehensively examined the impact of different referencing schemes and LFP features on the performance of hand kinematic decoding using a deep learning method. We used LFPs chronically recorded from the motor cortex area of a monkey while performing reaching tasks. Main results. Experimental results revealed that local motor potential (LMP) emerged as the most informative feature regardless of the referencing schemes. Using LMP as the feature, CAR was found to yield consistently better decoding performance than other referencing schemes over long-term recording sessions. Significance. Overall, our results suggest the potential use of LMP coupled with CAR for enhancing the decoding performance of LFP-based BMIs.
Highlights
Brain-machine interfaces (BMIs) have emerged as a promising technology to restore lost motor function in severely paralysed individuals due to neurological disorders such as amyotrophic lateral sclerosis (ALS), spinal cord injury (SCI), and stroke
We have presented a systematic and comprehensive investigation of the impact of the referencing schemes on the decoding performance of local field potential (LFP)-based BMI
Our experimental results revealed that local motor potential (LMP) was the most informative LFP feature regardless of the referencing schemes, indicating its richness of movement-related information
Summary
Brain-machine interfaces (BMIs) have emerged as a promising technology to restore lost motor function in severely paralysed individuals due to neurological disorders such as amyotrophic lateral sclerosis (ALS), spinal cord injury (SCI), and stroke. Impact of referencing scheme on decoding performance of LFP-based BMI as an alternative or complementary input signal for BMIs [1, 2, 3]. This trend arises from the desirable properties of LFP: signal stability and low bandwidth. LFPs can be sampled and processed at much lower sampling rate than spike, which translates into lower power consumption, thereby minimising the risk of overheating, reducing the form factor and increasing lifespan of a BMI device [1, 3]. Many researchers are interested in attempting to extract informative features for decoding kinetic or kinematic parameters of movement [6, 7, 2, 8]
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