A benchtop system to assess the feasibility of a fully independent and implantable brain-machine interface.

OBJECTIVE: State-of-the-art invasive brain-machine interfaces (BMIs) have shown significant promise, but rely on external electronics and wired connections between the brain and these external components. This configuration presents health risks and limits practical use. These limitations can be addressed by designing a fully implantable BMI similar to existing FDA-approved implantable devices. Here, a prototype BMI system whose size and power consumption are comparable to those of fully implantable medical devices was designed and implemented, and its performance was tested at the benchtop and bedside. APPROACH: A prototype of a fully implantable BMI system was designed and implemented as a miniaturized embedded system. This benchtop analogue was tested in its ability to acquire signals, train a decoder, perform online decoding, wirelessly control external devices, and operate independently on battery. Furthermore, performance metrics such as power consumption were benchmarked. MAIN RESULTS: An analogue of a fully implantable BMI was fabricated with a miniaturized form factor. A patient undergoing epilepsy surgery evaluation with an electrocorticogram (ECoG) grid implanted over the primary motor cortex was recruited to operate the system. Seven online runs were performed with an average binary state decoding accuracy of 87.0% (lag optimized, or 85.0% at fixed latency). The system was powered by a wirelessly rechargeable battery, consumed ∼150 mW, and operated for >60 h on a single battery cycle. SIGNIFICANCE: The BMI analogue achieved immediate and accurate decoding of ECoG signals underlying hand movements. A wirelessly rechargeable battery and other supporting functions allowed the system to function independently. In addition to the small footprint and acceptable power and heat dissipation, these results suggest that fully implantable BMI systems are feasible.

Electrocorticographic Activity of the Brain During Micturition.

Current therapies for neurogenic bladder do not allow spinal cord injury patients to regain conscious control of urine storage or voiding. Novel neural technologies may provide means to improve or restore the connection between the brain and the bladder; however, the specific brain areas and their underlying neural activities responsible for micturition must be better understood in order to design such technologies. In this retrospective study, we analyzed electrocorticographic (ECoG) data obtained from epilepsy patients who underwent ECoG grid implantation for epilepsy surgery evaluation, in the hopes of determining specific electrophysiological activity associated with micturition. Our results indicate modulation of the delta (δ, 0.1-4 Hz) and low-gamma (\gamma, 25-50 Hz) activity in the peri-Sylvian area and the inferior temporal lobe. These findings suggest involvement of the insular cortex and the uncinate fasciculus in micturition, important structures related to sensation and decision making. To date, this is the first known study utilizing ECoG data to elucidate the electrophysiological activity of the brain associated with bladder control and sensation.

Characterization of electrocorticogram high-gamma signal in response to varying upper extremity movement velocity.

The mechanism by which the human primary motor cortex (M1) encodes upper extremity movement kinematics is not fully understood. For example, human electrocorticogram (ECoG) signals have been shown to modulate with upper extremity movements; however, this relationship has not been explicitly characterized. To address this issue, we recorded high-density ECoG signals from patients undergoing epilepsy surgery evaluation as they performed elementary upper extremity movements while systematically varying movement speed and duration. Specifically, subjects performed intermittent pincer grasp/release, elbow flexion/extension, and shoulder flexion/extension at slow, moderate, and fast speeds. In all movements, bursts of power in the high-[Formula: see text] band (80-160 Hz) were observed in M1. In addition, the amplitude of these power bursts and the area of M1 with elevated high-[Formula: see text] activity were directly proportional to the movement speed. Likewise, the duration of elevated high-[Formula: see text] activity increased with movement duration. Based on linear regression, M1 high-[Formula: see text] power amplitude and duration covaried with movement speed and duration, respectively, with an average [Formula: see text] of [Formula: see text] and [Formula: see text]. These findings indicate that the encoding of upper extremity movement speed by M1 high-[Formula: see text] activity is primarily linear. Also, the fact that this activity remained elevated throughout a movement suggests that M1 does not merely generate transient instructions for a specific movement duration, but instead is responsible for the entirety of the movement. Finally, the spatial distribution of high-[Formula: see text] activity suggests the presence of a recruitment phenomenon in which higher speeds or increased muscle activity involve activation of larger M1 areas.

Sensitivity and specificity of upper extremity movements decoded from electrocorticogram.

Electrocorticogram (ECoG)-based brain computer interfaces (BCI) can potentially be used for control of arm prostheses. Restoring independent function to BCI users with such a system will likely require control of many degrees-of-freedom (DOF). However, our ability to decode many-DOF arm movements from ECoG signals has not been thoroughly tested. To this end, we conducted a comprehensive study of the ECoG signals underlying 6 elementary upper extremity movements. Two subjects undergoing ECoG electrode grid implantation for epilepsy surgery evaluation participated in the study. For each task, their data were analyzed to design a decoding model to classify ECoG as idling or movement. The decoding models were found to be highly sensitive in detecting movement, but not specific in distinguishing between different movement types. Since sensitivity and specificity must be traded-off, these results imply that conventional ECoG grids may not provide sufficient resolution for decoding many-DOF upper extremity movements.