Non-Invasive Brain-to-Brain Interface (BBI): Establishing Functional Links between Two Brains

Transcranial focused ultrasound (FUS) is capable of modulating the neural activity of specific brain regions, with a potential role as a non-invasive computer-to-brain interface (CBI). In conjunction with the use of brain-to-computer interface (BCI) techniques that translate brain function to generate computer commands, we investigated the feasibility of using the FUS-based CBI to non-invasively establish a functional link between the brains of different species (i.e. human and Sprague-Dawley rat), thus creating a brain-to-brain interface (BBI). The implementation was aimed to non-invasively translate the human volunteer's intention to stimulate a rat's brain motor area that is responsible for the tail movement. The volunteer initiated the intention by looking at a strobe light flicker on a computer display, and the degree of synchronization in the electroencephalographic steady-state-visual-evoked-potentials (SSVEP) with respect to the strobe frequency was analyzed using a computer. Increased signal amplitude in the SSVEP, indicating the volunteer's intention, triggered the delivery of a burst-mode FUS (350 kHz ultrasound frequency, tone burst duration of 0.5 ms, pulse repetition frequency of 1 kHz, given for 300 msec duration) to excite the motor area of an anesthetized rat transcranially. The successful excitation subsequently elicited the tail movement, which was detected by a motion sensor. The interface was achieved at 94.0 +/- 3.0% accuracy, with a time delay of 1.59 +/- 1.07 sec from the thought-initiation to the creation of the tail movement. Our results demonstrate the feasibility of a computer-mediated BBI that links central neural functions between two biological entities, which may confer unexplored opportunities in the study of neuroscience with potential implications for therapeutic applications.open12

Example of bio-signals obtained from the BBI operation.

<p>(A) Initiation of operator intension (as signaled by the finger movement; top), the raw EEG data (the 2^nd row), the filtered EEG data at 15 Hz (the 3^rd row), and the detected rat tail movement (the last row). The threshold condition for the filtered EEG is shown in dotted line. (B) The time resolved plot of the box shown in (A).</p

The schematics of the implemented brain-to-brain interface (BBI).

<p>The implementation consists of steady-state visual evoked potential (SSVEP)-based brain-to-computer interface (BCI: on the left column) and focused ultrasound (FUS)-based computer-to-brain interface (CBI) segments (on the right column).</p

Optimization of BCI parameter.

<p>Optimization of BCI parameter.</p

Distribution of AAC and F1-scores across the participants (n = 7) in various flickering frequencies and peak detection thresholds in terms of standard deviation (SD) of the baseline EEG signal.

<p>Distribution of AAC and F1-scores across the participants (n = 7) in various flickering frequencies and peak detection thresholds in terms of standard deviation (SD) of the baseline EEG signal.</p

An example of raw and filtered SSVEP.

<p>A raw SSVEP (in gray lines) and the signal after the application of the digital filter at the corresponding stimulation frequency (in black lines), obtained from a volunteer from four different stimulation frequencies (5, 10, 15 and 20 Hz). The rectangular box indicates the time the operator intended to engage the task.</p