A bi-directional neural interface (NI) system was designed and built by incorporating a novel neural recording and processing subsystem into a commercially approved neural stimulator. The NI system prototype leverages the system infrastructure from a market-approved neurostimulator to ensure reliable operation in a chronic implantation environment. In addition to providing approved therapy capabilities, the device adds key elements to facilitate chronic clinical research, such as four channels of ECoG/LFP amplification and spectral analysis, a three axis accelerometer, algorithm processing, event-based data logging, and wireless telemetry for data uploads and algorithm/configuration updates. The custom integrated micropower sensor and interface circuits facilitate extended operation in a power-limited device. The prototype underwent significant verification testing to ensure reliability, and meets the requirements for a class CF instrument per IEC-60601 protocols. The ability of the device system to process and aid in classifying brain states was preclinically validated using an in-vivo non-human primate model for brain control of a computer cursor (i.e., brain machine interface or BMI). The primate BMI model was chosen for its ability to quantitatively measure signal decoding performance from brain activity that is similar in both amplitude and spectral content to other biomarkers used to detect disease states (e.g. Parkinson’s). A key goal of this research prototype is to help broaden the clinical scope and acceptance of NI techniques, particularly real-time brain state detection. These techniques can be generalized beyond motor prosthesis, to include significant unmet needs in other neurological conditions such as movement disorders, stroke, and epilepsy.

Brain-computer interface (BCI) technology decodes neural signals in realtime to control external devices. In this study, chronic epidural micro-electrocorticographic (μECoG) recordings were performed over primary motor (M1) and dorsal premotor (PMd) cortex of three macaque monkeys. The differential gamma band amplitude (75–105 Hz) from two arbitrarily chosen 300μm electrodes (one located over each cortical area) was used for closed-loop control of a one degree-of-freedom BCI device. Each monkey rapidly learned over a period of days to successfully control the velocity of a computer cursor. While both cortical areas contributed to success on the BCI task, the control signals from M1 were consistently modulated more strongly than those from PMd. Additionally, we observe that gamma-band power during active BCI control is always above resting brain activity. This suggests that purposeful gamma-band modulation is an active process that is obtained through increased cortical activation.

The notion that a computer can decode brain signals to infer the intentions of a human and then enact those intentions directly through a machine is becoming a realistic technical possibility. These types of devices are known as brain-computer interfaces (BCIs). The evolution of these neuroprosthetic technologies could have significant implications for patients with motor disabilities by enhancing their ability to interact and communicate with their environment. The cortical physiology most investigated and used for device control has been brain signals from the primary motor cortex. To date, this classic motor physiology has been an effective substrate for demonstrating the potential efficacy of BCI-based control. However, emerging research now stands to further enhance our understanding of the cortical physiology underpinning human intent and provide further signals for more complex brain-derived control. In this review, the authors report the current status of BCIs and detail the emerging research trends that stand to augment clinical applications in the future.

Mirror neurons (MNs) have the distinguishing characteristic of modulating during both execution and observation of an action. Although most studies of MNs have focused on various features of the observed movement, MNs also may monitor the behavioral circumstances in which the movement is embedded, including time periods preceding and following the observed movement. Here, we recorded multiple MNs simultaneously from implanted electrode arrays as two male monkeys executed and observed a reach, grasp, and manipulate task involving different target objects. MNs were recorded from premotor cortex (PM-MNs) and primary motor cortex (M1-MNs). During execution trials, hidden Markov models (HMMs) applied to the activity of either PM-MN or M1-MN populations most often detected sequences of four hidden states, which we named according to the behavioral epoch during which each state began: initial, reaction, movement, and final. The hidden states of MN populations thus reflected not only the movement, but also three behavioral epochs during which no movement occurred. HMMs trained on execution trials could decode similar sequences of hidden states in observation trials, with complete hidden state sequences decoded more frequently from PM-MN populations than from M1-MN populations. Moreover, population trajectories projected in a 2D plane defined by execution trials were preserved in observation trials more for PM-MN than for M1-MN populations. These results suggest that MN populations represent entire behavioral sequences, including both movement and non-movement. PM-MN populations showed greater similarity than M1-MN populations in their representation of behavioral sequences during execution versus observation. Mirror neurons (MNs) are thought to provide a neural mechanism for understanding the actions of others. However, for an action to be understood, both the movement per se and the non-movement context before and after the movement need to be represented. We found that simultaneously recorded MN populations encoded sequential hidden neural states corresponding approximately to sequential behavioral epochs of a reach, grasp, and manipulate task. During observation trials, hidden state sequences were similar to those identified in execution trials. Hidden state similarity was stronger for MN populations in premotor cortex than for those in primary motor cortex. Execution/observation similarity of hidden state sequences may contribute to understanding the actions of others without actually performing the action oneself.

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