Reading the motor intention from brain activity within 100 ms

Utilizing sensory prediction errors for movement intention decoding.

Experiment to test the new technique:The researchers propose to use a sensory stimulator in parallel with EEG, and decode whether the stimulation matches (or not) the sensory feedback corresponding to the user's motor intention. The presented experiment simulated a wheelchair turning scenario, and utilized a galvanic vestibular stimulator (GVS). B) The subjects were affixed with GVS electrodes during the EEG recording. The subliminal GVS stimulations induced a sensation of turning either right or left. C) Experiment timeline: In each trial, using stereo speakers and a ‛high' frequency beep, the subjects were instructed to imagine turning either left or right while sitting on a wheelchair. A subliminal GVS stimulation was applied 2 seconds after the end of each cue, randomly either corresponding to turning right or left. This was followed by a rest period of 3 seconds cued by a ‛low' frequency beep (stop cue).
Experiment to test the new technique:The researchers propose to use a sensory stimulator in parallel with EEG, and decode whether the stimulation matches (or not) the sensory feedback corresponding to the user's motor intention. The presented experiment simulated a wheelchair turning scenario, and utilized a galvanic vestibular stimulator (GVS). B) The subjects were affixed with GVS electrodes during the EEG recording. The subliminal GVS stimulations induced a sensation of turning either right or left. C) Experiment timeline: In each trial, using stereo speakers and a ‛high' frequency beep, the subjects were instructed to imagine turning either left or right while sitting on a wheelchair. A subliminal GVS stimulation was applied 2 seconds after the end of each cue, randomly either corresponding to turning right or left. This was followed by a rest period of 3 seconds cued by a ‛low' frequency beep (stop cue).

The main challenge of Brain-Computer Interface is to decipher of a human user’s movement intention from his brain activity while minimizing the user effort. Although there are different methods demonstrated, they all require a large effort in part of the human user.

Now, scientists at the Tokyo Tech have devised a new technique that enables a user to decode motor intention of humans from Electroencephalography (EEG). The study proposed a new invention that addresses previous issues while providing equally much better-decoding performance.

Scientists took inspiration from the human brain that has the capability of predicting sensory outcomes of self-generated and imagined actions utilizing so-called forward models. The method, in addition, nearly 90% single trial decoding accuracy across tested subjects, within 96 ms of the stimulation.

Decoding performance summary:The across subject median decoding performance when decoding for the direction in which a subject wants to turn (ie. the cue direction), as has been tried in previous methods, is shown in red and pink, while decoding using the new proposed method is shown in black. The data at each time point represents the decoding performance using data from the time period between a reference point (‛cue' for red data, and ‛GVS start' for pink and black data) and that time point. Box plot boundaries represent the 25th and 75th percentile, while the whiskers represent the data range across subjects. The inset histograms shows the subject ensemble decoding performance in the 140 (twenty X7 subjects) test trials, with each subject data shown in a different color.
Decoding performance summary:The across subject median decoding performance when decoding for the direction in which a subject wants to turn (ie. the cue direction), as has been tried in previous methods, is shown in red and pink, while decoding using the new proposed method is shown in black. The data at each time point represents the decoding performance using data from the time period between a reference point (‛cue’ for red data, and ‛GVS start’ for pink and black data) and that time point. Box plot boundaries represent the 25th and 75th percentile, while the whiskers represent the data range across subjects. The inset histograms shows the subject ensemble decoding performance in the 140 (twenty X7 subjects) test trials, with each subject data shown in a different color.

Scientists here proposes a subliminal sensory stimulator with the Electroencephalography (EEG), to decode whether the movement he intends matches (or not) the sensory feedback sent to the user using the stimulator. The specialists in this way estimated the predictions errors to have a large signature in EEG and irritating the prediction errors (utilizing an outside tangible trigger) to be a promising method to interpret development goals.

This proposal originally was motivated by the multitude of studies on so-called Forward models in the brain; the neural circuitry implicated in predicting sensory outcomes of self-generated movements (3). The sensory prediction errors, between the forward model predictions and the actual sensory signals, are known to be fundamental for our sensory-motor abilities- for haptic perception (4), motor control (5), motor learning (6), and even inter-personal interactions (7-8) and the cognition of self (9).

Scientists then tested the proposal in a binary simulated wheelchair task, where users need to turn their wheelchair either left or right. During the task, scientists stimulated the user’s vestibular system, towards either the left or right direction, subliminally using a galvanic vestibular stimulator.

Based on that scientists decoded prediction errors (ie. whether or stimulation direction matches the direction the user imagines, or not) and consequently, as the direction of stimulation is known, the direction the user imagines. This procedure provides excellent single trial decoding accuracy (87.2% median) in all tested subjects, and within 96 ms of stimulation. These results were obtained with zero user training and with no additional cognitive load on the users, as the stimulation was subliminal.

Scientists noted, “the proposal promises to radically change how movement intention is decoded, due to several reasons. Primarily, because the method promises better decoding accuracies with no user training and without inducing additional cognitive loads on the users.”

The study is published in Science Advances.