It is well-known that motor cortical oscillatory components are modulated in their amplitude during voluntary and imagined movements. These patterns have been used to develop brain-machine interfaces (BMI) which focused mostly on movement kinematics. In contrast, there have been only a few studies on the relation between brain oscillatory activity and the control of force, in particular, grasping force, which is of primary importance for common daily activities. In this study, we recorded intraoperative high-density electrocorticography (ECoG) from the sensorimotor cortex of four patients while they executed a voluntary isometric hand grasp following verbal instruction. The grasp was held for 2 to 3 s before being instructed to relax. We studied the power modulations of neural oscillations during the whole time-course of the grasp (onset, hold, and offset phases). Phasic event-related desynchronization (ERD) in the low-frequency band (LFB) from 8 to 32 Hz and event-related synchronization (ERS) in the high-frequency band (HFB) from 60 to 200 Hz were observed at grasp onset and offset. However, during the grasp holding period, the magnitude of LFB-ERD and HFB-ERS decreased near or at the baseline level. Overall, LFB-ERD and HFB-ERS show phasic characteristics related to the changes of grasp force (onset/offset) in all four patients. More precisely, the fluctuations of HFB-ERS primarily, and of LFB-ERD to a lesser extent, correlated with the time-course of the first time-derivative of force (yank), rather than with force itself. To the best of our knowledge, this is the first study that establishes such a correlation. These results have fundamental implications for the decoding of grasp in brain oscillatory activity-based neuroprosthetics.

Objective. Somatosensory evoked potentials (SSEPs) recorded with electrocorticography (ECoG) for central sulcus (CS) identification is a widely accepted procedure in routine intraoperative neurophysiological monitoring. Clinical practices test the short-latency SSEPs for the phase reversal over strip electrodes. However, assessments based on waveform morphology are susceptible to variations in interpretations due to the hand area’s localized nature and usually require multiple electrode placements or electrode relocation. We investigated the feasibility of unsupervised delineation of the CS by using the spatiotemporal patterns of the SSEP captured with the ECoG grid. Approach. Intraoperatively, SSEPs were recorded from eight patients using ECoG grids placed over the sensorimotor cortex. Neurosurgeons blinded to the electrophysiology identified the sensory and motor gyri using neuronavigation based on sulcal anatomy. We quantified the most discriminatory time points in SSEPs temporal profile between the primary motor (M1) and somatosensory (S1) cortex using the Fisher discrimination criterion. We visualized the amplitude gradient of the SSEP over a 2D heat map to provide visual feedback for the delineation of the CS based on electrophysiology. Subsequently, we employed spectral clustering using the entire the SSEP waveform without selecting any time points and grouped ECoG channels in an unsupervised fashion. Main results. Consistently in all patients, two different time points provided almost equal discrimination between anterior and posterior channels, which vividly outlined the CS when we viewed the SSEP amplitude distribution as a spatial 2D heat map. The first discriminative time point was in proximity to the conventionally favored ∼20 ms peak (N20), and the second time point was slightly later than the markedly high ∼30 ms peak (P30). Still, the location of these time points varied noticeably across subjects. Unsupervised clustering approach separated the anterior and posterior channels with an accuracy of 96.3% based on the time derivative of the SSEP trace without the need for a subject-specific time point selection. In contrast, the raw trace resulted in an accuracy of 88.0%. Significance. We show that the unsupervised clustering of the SSEP trace assessed with subdural electrode grids can delineate the CS automatically with high precision, and the constructed heat maps can localize the motor cortex. We anticipate that the spatiotemporal patterns of SSEP fused with machine learning can serve as a useful tool to assist in surgical planning.

Recording from the human brain at the spatiotemporal resolution of action potentials provides critical insight into mechanisms of higher cognitive functions and neuropsychiatric disease that is challenging to derive from animal models. Here, organic materials and conformable electronics are employed to create an integrated neural interface device compatible with minimally invasive neurosurgical procedures and geared toward chronic implantation on the surface of the human brain. Data generated with these devices enable identification and characterization of individual, spatially distribute human cortical neurons in the absence of any tissue penetration (n = 229 single units). Putative single‐units are effectively clustered, and found to possess features characteristic of pyramidal cells and interneurons, as well as identifiable microcircuit interactions. Human neurons exhibit consistent phase modulation by oscillatory activity and a variety of population coupling responses. The parameters are furthermore established to optimize the yield and quality of single‐unit activity from the cortical surface, enhancing the ability to investigate human neural network mechanisms without breaching the tissue interface and increasing the information that can be safely derived from neurophysiological monitoring.

This paper presents a portable platform to collect and review behavioral data simultaneously with neurophysiological signals. The whole system is comprised of four parts: a sensor data acquisition interface, a socket server for real-time data streaming, a Simulink system for real-time processing and an offline data review and analysis toolbox. A low-cost microcontroller is used to acquire data from external sensors such as accelerometer and hand dynamometer. The micro-controller transfers the data either directly through USB or wirelessly through a bluetooth module to a data server written in C++ for MS Windows OS. The data server also interfaces with the digital glove and captures HD video from webcam. The acquired sensor data are streamed under User Datagram Protocol (UDP) to other applications such as Simulink/Matlab for real-time analysis and recording. Neurophysiological signals such as electroencephalography (EEG), electrocorticography (ECoG) and local field potential (LFP) recordings can be collected simultaneously in Simulink and fused with behavioral data. In addition, we developed a customized Matlab Graphical User Interface (GUI) software to review, annotate and analyze the data offline. The software provides a fast, user-friendly data visualization environment with synchronized video playback feature. The software is also capable of reviewing long-term neural recordings. Other featured functions such as fast preprocessing with multithreaded filters, annotation, montage selection, power-spectral density (PSD) estimate, time-frequency map and spatial spectral map are also implemented.

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