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There are few EEG studies on finger movement directions because ocular artifacts also convey directional information, which makes it hard to separate the contribution of EEG from that of the ocular artifacts. To overcome this issue, we designed an experiment in which EEG’s temporal dynamics and spatial information are evaluated together to improve the performance of brain-computer interface (BCI) for classifying finger movement directions. Six volunteers participated in the study. We examined their EEG using decoding analyses. Independent components (ICs) that represented brain-source signals successfully classified the directions of the finger movements with higher rates than chance level. The weight analyses of the classifiers revealed that maximal performance of the classification was recorded at the latencies prior to the onset of finger movements. The weight analyses also revealed the relevant cortical areas including the right lingual, left posterior cingulate, left inferior temporal gyrus, and right precuneus, which indicated the involvement of the visuospatial processing. We concluded that combining spatial distribution and temporal dynamics of the scalp EEG may improve BCI performance.

This article describes a novel type of an advanced mobile security system, a myokinetic password, using surface electromyographic (EMG) signals. We proposed an advanced security system that can increase password strength without modifying password length or increasing alphabet character variation. We present an implementation of an intuitive password system that combines muscle activation to enhance password complexity. The muscle activation levels from EMG signals are used to split each individual code composition into multiple states, thereby increasing possible password combinations. We proposed a centroid-based clustering algorithm to cluster a feature into groups according to the muscle activation. We also verified the possibility of expanding the technique to support up to three muscle activation levels in the experiment. By utilizing the proposed method, the number of possible input key combination is increased from 256 to 810,000 with three muscle levels.

Proceedings Of The 6Th International Bci Meeting: Bci Past, Present, And Future

There is a growing interest in how the brain transforms body part positioning in the extrinsic environment into an intrinsic coordinate frame during motor control. To explore the human brain areas representing intrinsic and extrinsic coordinate frames, this fMRI study examined neural representation of motor cortices while human participants performed isometric wrist flexions and extensions in different forearm postures, thereby applying the same wrist actions (representing the intrinsic coordinate frame) to different movement directions (representing the extrinsic coordinate frame). Using sparse logistic regression, critical voxels involving pattern information that specifically discriminates wrist action (flexion vs. extension) and movement direction (upward vs. downward) were identified within the primary motor and premotor cortices. Analyses of classifier weights further identified contributions of the primary motor cortex to the intrinsic coordinate frame and the ventral and dorsal premotor cortex and supplementary motor area proper to the extrinsic coordinate frame. These results are consistent with existing findings using non-human primates and demonstrate the distributed representations of independent coordinate frames in the human brain.

Brain-machine interface techniques have been applied in a number of studies to control neuromotor prostheses and for neurorehabilitation in the hopes of providing a means to restore lost motor function. Electrocorticography (ECoG) has seen recent use in this regard because it offers a higher spatiotemporal resolution than non-invasive EEG and is less invasive than intracortical microelectrodes. Although several studies have already succeeded in the inference of computer cursor trajectories and finger flexions using human ECoG signals, precise three-dimensional (3D) trajectory reconstruction for a human limb from ECoG has not yet been achieved. In this study, we predicted 3D arm trajectories in time series from ECoG signals in humans using a novel preprocessing method and a sparse linear regression. Average Pearson’s correlation coefficients and normalized root-mean-square errors between predicted and actual trajectories were 0.44∼0.73 and 0.18∼0.42, respectively, confirming the feasibility of predicting 3D arm trajectories from ECoG. We foresee this method contributing to future advancements in neuroprosthesis and neurorehabilitation technology.