Extracted Audio-Induced Reward Expectation Information from Local Field Potential in the Medial Prefrontal Cortex

Brain-machine interface (BMI) technology has witnessed notable advancements, facilitating individuals with motor disabilities to effectively operate prosthetic limbs. Reinforcement learning (RL) has been employed within BMIs to train decoders that can interpret neural activity and translate it into movement intentions using reward information. Internal rewards, reflected in neural response to sensory feedback in the medial prefrontal cortex (mPFC), can be used for autonomous updates in RL-BMIs. Studies have shown that designed audio feedback improves subjects' learning abilities, while neural activity in the mPFC induced by audio feedback indicates future rewards information. These findings highlight the possibility to utilized neural modulation in mPFC upon audio-induced can serves intermediate guidance on decoder update feedback. However, the reliance on single neuron spike signals from mPFC has limitations and will be unavailable, especially in long-term BMI implants. Local field potentials (LFPs) provide neural ensemble information and have been proposed as an alternative long-term data source to overcome these limitations. This paper proposes to extract LFPs neuromodulations and relate them to audio-induced reward expectation information from mPFC neural activity by implementing a data-driven marked point process (MPP) methodology. We correlate synchronized spike activity to the transient events in the LFP broad high frequency (bhf) band (200-400Hz) in the mPFC of rats performing the two-lever press discrimination task. Compared with extracting LFP features from the binned spectrogram power, our approach improves 24.63% on average in the peak-signal-to-noise-ratio (PSNR) across subjects over our data segments. This study indicates that LFPs in the mPFC contains the information that can provide sensory-induced reward expectation information in long-term use and advances the development of autonomous guidance for BMI decoders.