Paradigm Shift in Sensorimotor Control Research and Brain Machine Interface Control: The Influence of Context on Sensorimotor Representations

Neural activity in the primary motor cortex (M1) is known to correlate with movement related variables including kinematics and dynamics. Our recent work, which we believe is part of a paradigm shift in sensorimotor research, has shown that in addition to these movement related variables, activity in M1 and the primary somatosensory cortex (S1) are also modulated by context, such as value, during both active movement and movement observation. Here we expand on the investigation of reward modulation in M1, showing that reward level changes the neural tuning function of M1 units to both kinematic as well as dynamic related variables. In addition, we show that this reward-modulated activity is present during brain machine interface (BMI) control. We suggest that by taking into account these context dependencies of M1 modulation, we can produce more robust BMIs. Toward this goal, we demonstrate that we can classify reward expectation from M1 on a movement-by-movement basis under BMI control and use this to gate multiple linear BMI decoders toward improved offline performance. These findings demonstrate that it is possible and meaningful to design a more accurate BMI decoder that takes reward and context into consideration. Our next step in this development will be to incorporate this gating system, or a continuous variant of it, into online BMI performance

Utilizing microstimulation and local field potentials in the primary somatosensory and motor cortex

Brain-computer interfaces (BCIs) have advanced considerably from simple target detection by recording from a single neuron, to accomplishments like controlling a computer cursor accurately with neural activity from hundreds of neurons or providing instruction directly to the brain via microstimulation. However as BCIs continue to evolve, so do the challenges they face. Most BCIs rely on visual feedback, requiring sustained visual attention to use the device. As the role of BCIs expands beyond cursors moving on a computer screen to robotic hands picking up objects, there is increased need for an effective way to provide quick feedback independent of vision. Another challenge is utilizing all the signals available to produce the best decoding of movement possible. Local field potentials (LFPs) can be recorded at the same time as multi-unit activity (MUA) from multielectrode arrays but little is known in the area of what kind of information it possess, especially in relation to MUA. To tackle these issues, we preformed the following experiments. First, we examined the effectiveness of alternative forms of feedback applicable to BCIs, tactile stimuli delivered on the skin surface and microstimulation applied directly to the brain via the somatosensory cortex. To gauge effectiveness, we used a paradigm that captured a fundamental element of feedback: the ability to react to a stimulus while already in action. By measuring the response time to that stimulus, we were able to compare how well each modality could perform as a feedback stimulus. Second, we use regression and mutual information analyses to study how MUA, low-frequency LFP (15-40Hz, LFPL ), and high-frequency LFP (100-300Hz, LFPH) encoded reaching movements. The representation of kinematic parameters for direction, speed, velocity, and position were quantified and compared across these signals to be better applied in decoding models. Lastly, the results from these experiments could not have been accurately obtained without keeping careful account of the mechanical lags involved. Each of the stimuli affecting behavior had onset lags, which in some cases, varied greatly from trial to trial and could easily distorted timing effects if not accounted for. Special adaptations were constructed to precisely pinpoint display, system, and device onset lags