Volitional control of neural activity: implications for brain-computer interfaces

Abstract

Successful operation of brain-computer interfaces (BCI) and brain-machine interfaces (BMI) depends significantly on the degree to which neural activity can be volitionally controlled. This paper reviews evidence for such volitional control in a variety of neural signals, with particular emphasis on the activity of cortical neurons. Some evidence comes from conventional experiments that reveal volitional modulation in neural activity related to behaviours, including real and imagined movements, cognitive imagery and shifts of attention. More direct evidence comes from studies on operant conditioning of neural activity using biofeedback, and from BCI/BMI studies in which neural activity controls cursors or peripheral devices. Limits in the degree of accuracy of control in the latter studies can be attributed to several possible factors. Some of these factors, particularly limited practice time, can be addressed with long-term implanted BCIs. Preliminary observations with implanted circuits implementing recurrent BCIs are summarized.

Figure 1

Operant conditioning of differential firing rates of two neighbouring motor cortex neurons Points plot 1 min average rates of large and small unit (L and S). ‘Operant level’ is activity prior to conditioning, with monkey seated in primate chair. Reinforcement periods are labelled by ‘↑’ and ‘↓’ indicating whether activity of the unit drove the biofeedback meter arm towards or away from level for triggering feeder. During ‘SΔ’ (time-out) periods feedback meter and feeder were turned off. (Used with permission from Fetz & Baker, 1973.)

Figure 2

Basic components of operant conditioning biofeedback paradigm Feedback and reward are contingent on the reinforced activity and provided to the brain of the ‘Volitional controller’. The correlated activity consists of additional neural or physiological activity either causally or adventitiously associated with the reinforced activity.

Figure 3

Accuracy of predicting movement parameters as functions of increasing number of neurons from different cortical areas Each curve represents the correlation between the actual parameter and linear prediction based on activity of cells from particular cortical areas (PMd, dorsal premotor cortex; M1, primary motor cortex; S1, primary somatosensory cortex; SMA, supplementary motor area; PP, posterior parietal cortex). Average correlation was computed for increasing number of randomly chosen neurons. (Data from Carmena et al. 2003).

Figure 4

Basic components of the BCI and BMI paradigm Essential components are identical to those of the biofeedback paradigm, except that feedback (usually visual) is provided by the controlled device or cursor and a more sophisticated transform algorithm is typically used to convert neural activity to the requisite control signals.

Figure 5

Continuous operation of a cortical recurrent BCI leads to long-lasting changes in physiological connections Top: intracranial microstimulation at 3 different motor cortex sites with the monkey at rest evoked 3 different muscle responses (centre) and different isometric torques about the wrist (right). Arrows at right indicate means of 200 ms torque trajectories. Middle: conditioning involved 2 days of triggering microstimuli at site Nstim for every spike recorded at Nrec during free behaviour and sleep. Bottom: after conditioning the output effects evoked from site Nrec had changed to include those from Nstim, an effect that lasted beyond a week. A plausible mechanism is Hebbian strengthening of synaptic connections from Nrec to Nstim. (For further details see Jackson et al. 2006a.)