Brain–machine interfaces (BMIs) employ the electrical activity generated by cortical neurons directly for controlling external devices and have been conceived as a means for restoring human cognitive or sensory-motor functions. The dominant approach in BMI research has been to decode motor variables based on single-unit activity (SUA). Unfortunately, this approach suffers from poor long-term stability and daily recalibration is normally required to maintain reliable performance. A possible alternative is BMIs based on electrocorticograms (ECoGs), which measure population activity and may provide more durable and stable recording. However, the level of long-term stability that ECoG-based decoding can offer remains unclear. Here we propose a novel ECoG-based decoding paradigm and show that we have successfully decoded hand positions and arm joint angles during an asynchronous food-reaching task in monkeys when explicit cues prompting the onset of movement were not required. Performance using our ECoG-based decoder was comparable to existing SUA-based systems while evincing far superior stability and durability. In addition, the same decoder could be used for months without any drift in accuracy or recalibration. These results were achieved by incorporating the spatio-spectro-temporal integration of activity across multiple cortical areas to compensate for the lower fidelity of ECoG signals. These results show the feasibility of high-performance, chronic and versatile ECoG-based neuroprosthetic devices for real-life applications. This new method provides a stable platform for investigating cortical correlates for understanding motor control, sensory perception, and high-level cognitive processes.

Accumulating evidence indicates that the capacity to integrate information in the brain is a prerequisite for consciousness. Integrated Information Theory (IIT) of consciousness provides a mathematical approach to quantifying the information integrated in a system, called integrated information, Φ. Integrated information is defined theoretically as the amount of information a system generates as a whole, above and beyond the amount of information its parts independently generate. IIT predicts that the amount of integrated information in the brain should reflect levels of consciousness. Empirical evaluation of this theory requires computing integrated information from neural data acquired from experiments, although difficulties with using the original measure Φ precludes such computations. Although some practical measures have been previously proposed, we found that these measures fail to satisfy the theoretical requirements as a measure of integrated information. Measures of integrated information should satisfy the lower and upper bounds as follows: The lower bound of integrated information should be 0 and is equal to 0 when the system does not generate information (no information) or when the system comprises independent parts (no integration). The upper bound of integrated information is the amount of information generated by the whole system. Here we derive the novel practical measure Φ* by introducing a concept of mismatched decoding developed from information theory. We show that Φ* is properly bounded from below and above, as required, as a measure of integrated information. We derive the analytical expression of Φ* under the Gaussian assumption, which makes it readily applicable to experimental data. Our novel measure Φ* can generally be used as a measure of integrated information in research on consciousness, and also as a tool for network analysis on diverse areas of biology.

Synchronous oscillatory activity has been observed in a range of neural networks from invertebrate nervous systems to the human frontal cortex. In humans and other primates, sensorimotor regions of the neocortex exhibit synchronous oscillations in the beta-frequency band (approximately 15-30 Hz), and these are also prominent in the cerebellum, a brainstem sensorimotor region. However, recordings in the basal ganglia have suggested that such beta-band oscillations are not normally a primary feature of these structures. Instead, they become a dominant feature of neural activity in the basal ganglia in Parkinson's disease and in parkinsonian states induced by dopamine depletion in experimental animals. Here we demonstrate that when multiple electrodes are used to record local field potentials, 10-25 Hz oscillations can be readily detected in the striatum of normal macaque monkeys. These normally occurring oscillations are highly synchronous across large regions of the striatum. Furthermore, they are subject to dynamic modulation when monkeys perform a simple motor task to earn rewards. In the striatal region representing oculomotor activity, we found that small focal zones could pop in and out of synchrony as the monkeys made saccadic eye movements, suggesting that the broadly synchronous oscillatory activity interfaces with modular spatiotemporal patterns of task-related activity. We suggest that the background beta-band oscillations in the striatum could help to focus action-selection network functions of cortico-basal ganglia circuits.