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We present non-invasive means that detect unilateral hand motor brain activity from one individual and subsequently stimulate the somatosensory area of another individual, thus, enabling the remote hemispheric link between each brain hemisphere in humans. Healthy participants were paired as a sender and a receiver. A sender performed a motor imagery task of either right or left hand, and associated changes in the electroencephalogram (EEG) mu rhythm (8–10 Hz) originating from either hemisphere were programmed to move a computer cursor to a target that appeared in either left or right of the computer screen. When the cursor reaches its target, the outcome was transmitted to another computer over the internet, and actuated the focused ultrasound (FUS) devices that selectively and non-invasively stimulated either the right or left hand somatosensory area of the receiver. Small FUS transducers effectively allowed for the independent administration of stimulatory ultrasonic waves to somatosensory areas. The stimulation elicited unilateral tactile sensation of the hand from the receiver, thus establishing the hemispheric brain-to-brain interface (BBI). Although there was a degree of variability in task accuracy, six pairs of volunteers performed the BBI task in high accuracy, transferring approximately eight commands per minute. Linkage between the hemispheric brain activities among individuals suggests the possibility for expansion of the information bandwidth in the context of BBI.

Neuronal source estimation is a general tool for analyzing spatiotemporal dynamics in human EEG. Despite rapidly-evolving interest in human brain, there are few EEG based source estimation tools in rodent brain. Therefore, we implemented source estimation tool in a mouse model, using the FieldTrip open-source software. High resolution EEGs with a known cortical source were recorded with a recently developed 40-channel polyimide-based microelectrode under optical stimulation on optogenetially engineered mice. To obtain realistic mouse head models, the volume conduction model was extracted from in vitro mouse brain MRIs. Segmented compartments (skin and outer/inner skull) were used to form triangular meshes and then applied to the boundary element method. The high-resolution EEGs recorded during various optogenetic stimulation of the mouse brain were inversely source reconstructed using minimum-norm estimate. Estimated source locations and strengths were reconstructed, and their error was calculated to evaluate FieldTrip-based source localization algorithm. In summary, source localization imaging of the mouse brain was successfully achieved, using freely-available open source software. This will be useful to investigate the functional dynamics of mouse brain in noninvasive measure.

It is well established that motor action/imagery provokes an event-related desynchronization (ERD) response at specific brain areas with specific frequency ranges, typically the sensory motor rhythm and beta bands. However, there are individual differences in both brain areas and frequency ranges which can be used to identify ERD. This often results in low classification accuracy of ERD, which makes it difficult to implement of BCI application such as the control of external devices and motor rehabilitation. To overcome this problem, an individually optimized solution may be desirable for enhancing the accuracy of detecting motor action/imagery with ERD rather than a global solution for all BCI users. This paper presents a method based on a genetic algorithm to find individually optimized brain areas and frequency ranges for ERD classification. To optimize these two components, we designed a chromosome consisting of 64-bit elements represented by a binary number and another 9-bit elements using 512 pre-defined frequency ranges (2^9). The average value of the significant level is set for the properties of the objective function for use in a t-test, (p < 0.01) depending on the random selection from a concurrent population. As a result, contralateral ERD responses in the spatial domain with individually optimized frequency ranges showed a significant difference between resting and motor action. The ERD responses for motor imagery, on the other hand, led to a bilateral pattern with a narrow frequency band compared to motor action. This study provides the possibility of selecting optimized electrode positions and frequency bands which can lead to high levels of ERD classification accuracy.