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Objective. Brain–computer interfaces (BCIs) have the potential to bypass damaged neural pathways and restore functionality lost due to injury or disease. Approaches to decoding kinematic information are well documented; however, the decoding of kinetic information has received less attention. Additionally, the possibility of using stereo-electroencephalography (SEEG) for kinetic decoding during hand grasping tasks is still largely unknown. Thus, the objective of this paper is to demonstrate kinetic parameter decoding using SEEG in patients performing a grasping task with two different force levels under two different ascending rates. Approach. Temporal-spectral representations were studied to investigate frequency modulation under different force tasks. Then, force amplitude was decoded from SEEG recordings using multiple decoders, including a linear model, a partial least squares model, an unscented Kalman filter, and three deep learning models (shallow convolutional neural network, deep convolutional neural network and the proposed CNN+RNN neural network). Main results. The current study showed that: (a) for some channel, both low-frequency modulation (event-related desynchronization (ERD)) and high-frequency modulation (event-related synchronization) were sustained during prolonged force holding periods; (b) continuously changing grasp force can be decoded from the SEEG signals; (c) the novel CNN+RNN deep learning model achieved the best decoding performance, with the predicted force magnitude closely aligned to the ground truth under different force amplitudes and changing rates. Significance. This work verified the possibility of decoding continuously changing grasp force using SEEG recordings. The result presented in this study demonstrated the potential of SEEG recordings for future BCI application.

Objective. Accurate identification of functional cortical regions is essential in neurological resection. The central sulcus (CS) is an important landmark that delineates functional cortical regions. Median nerve stimulation (MNS) is a standard procedure to identify the position of the CS intraoperatively. In this paper, we introduce an automated procedure that uses MNS to rapidly localize the CS and create functional somatotopic maps. Approach. We recorded electrocorticographic signals from 13 patients who underwent MNS in the course of an awake craniotomy. We analyzed these signals to develop an automated procedure that determines the location of the CS and that also produces functional somatotopic maps. Main results. The comparison between our automated method and visual inspection performed by the neurosurgeon shows that our procedure has a high sensitivity (89%) in identifying the CS. Further, we found substantial concordance between the functional somatotopic maps generated by our method and passive functional mapping (92% sensitivity). Significance. Our automated MNS-based method can rapidly localize the CS and create functional somatotopic maps without imposing additional burden on the clinical procedure. With additional development and validation, our method may lead to a diagnostic tool that guides neurosurgeons and reduces postoperative morbidity in patients undergoing resective brain surgery.

Objective. White matter tissue takes up approximately 50% of the human brain volume and it is widely known as a messenger conducting information between areas of the central nervous system. However, the characteristics of white matter neural activity and whether white matter neural recordings can contribute to movement decoding are often ignored and still remain largely unknown. In this work, we make quantitative analyses to investigate these two important questions using invasive neural recordings.Approach. We recorded stereo-electroencephalography (SEEG) data from 32 human subjects during a visually-cued motor task, where SEEG recordings can tap into gray and white matter electrical activity simultaneously. Using the proximal tissue density method, we identified the location (i.e. gray or white matter) of each SEEG contact. Focusing on alpha oscillatory and high gamma activities, we compared the activation patterns between gray matter and white matter. Then, we evaluated the performance of such white matter activation in movement decoding.Main results. The results show that white matter also presents activation under the task, in a similar way with the gray matter but at a significantly lower amplitude. Additionally, this work also demonstrates that combing white matter neural activities together with that of gray matter significantly promotes the movement decoding accuracy than using gray matter signals only.Significance. Taking advantage of SEEG recordings from a large number of subjects, we reveal the response characteristics of white matter neural signals under the task and demonstrate its enhancing function in movement decoding. This study highlights the importance of taking white matter activities into consideration in further scientific research and translational applications.