Signal recovery from stimulation artifacts in intracranial recordings with dictionary learning.

OBJECTIVE: Electrical stimulation of the human brain is commonly used for eliciting and inhibiting neural activity for clinical diagnostics, modifying abnormal neural circuit function for therapeutics, and interrogating cortical connectivity. However, recording electrical signals with concurrent stimulation results in dominant electrical artifacts that mask the neural signals of interest. Here we develop a method to reproducibly and robustly recover neural activity during concurrent stimulation. We concentrate on signal recovery across an array of electrodes without channel-wise fine-tuning of the algorithm. Our goal includes signal recovery with trains of stimulation pulses, since repeated, high-frequency pulses are often required to induce desired effects in both therapeutic and research domains. We have made all of our code and data publicly available. APPROACH: We developed an algorithm that automatically detects templates of artifacts across many channels of recording, creating a dictionary of learned templates using unsupervised clustering. The artifact template that best matches each individual artifact pulse is subtracted to recover the underlying activity. To assess the success of our method, we focus on whether it extracts physiologically interpretable signals from real recordings. MAIN RESULTS: We demonstrate our signal recovery approach on invasive electrophysiologic recordings from human subjects during stimulation. We show the recovery of meaningful neural signatures in both electrocorticographic (ECoG) arrays and deep brain stimulation (DBS) recordings. In addition, we compared cortical responses induced by the stimulation of primary somatosensory (S1) by natural peripheral touch, as well as motor cortex activity with and without concurrent S1 stimulation. SIGNIFICANCE: Our work will enable future advances in neural engineering with simultaneous stimulation and recording.

Concurrent control of a brain-computer interface and natural overt movements.

OBJECTIVE: A primary control signal in brain-computer interfaces (BCIs) have been cortical signals related to movement. However, in cases where natural motor function remains, BCI control signals may interfere with other possibly simultaneous activity for useful ongoing movement. We sought to determine if the brain could learn to control both a BCI and concurrent overt movement execution in such cases. APPROACH: We designed experiments where BCI and overt movements must be used concurrently and in coordination to achieve a 2D centre out control. Power in the 70-90 Hz band of human electrocorticography (ECoG) signals, was used to generate BCI control commands for vertical movement of the cursor. These signals were deliberately recorded from the same human cortical site that produced the strongest movement related activity associated with the concurrent overt finger movements required for the horizontal movement of the cursor. MAIN RESULTS: We demonstrate that three subjects were able to perform the concurrent BCI task, controlling BCI and natural movements simultaneously and to a large extent independently. We conclude that the brain is capable of dissociating the original control signal dependency on movement, producing specific BCI control signals in the presence of motor related responses from the ongoing overt behaviour with which the BCI signal was initially correlated. SIGNIFICANCE: We demonstrate a novel human brain-computer interface (BCI) which can be used to control movement concurrently and in coordination with movements of the natural limbs. This demonstrates the dissociation of cortical activity from the behaviour with which it was originally associated despite the ongoing behaviour and shows the feasibility of achieving simultaneous BCI control of devices with natural movements.

Motion detection and prediction through spike-timing dependent plasticity.

We describe a possible mechanism for the formation of direction- and velocity-selective cells in visual cortex through spike-timing dependent learning. We contrast the case where only feedforward excitation and inhibition signals are provided to visual neurons with the case where both feedforward and feedback signals are provided. In the feedforward-only case, neurons become selective for a broad range of velocities centered around the training velocity. However, we show that direction selectivity in this case is strongly dependent on delayed feedforward inhibition and in contrast to experimental results, becomes dramatically weaker when inhibition is reduced. When feedback connections are introduced, direction selectivity becomes much more robust due to predictive delays encoded in recurrent activity. Direction selectivity persists in the face of decreasing inhibition in a manner similar to experimental findings. The model predicts that direction-selective cells should exhibit anticipatory activity due to recurrent excitation and suggests a pivotal role for spike-timing dependent plasticity in shaping cortical circuits for visual motion detection and prediction.