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Most P300 event-related potential (ERP)-based brain–computer interface (BCI) studies focus on gaze shift-dependent BCIs, which cannot be used by people who have lost voluntary eye movement. However, the performance of visual saccade-independent P300 BCIs is generally poor. To improve saccade-independent BCI performance, we propose a bimodal P300 BCI approach that simultaneously employs auditory and tactile stimuli. The proposed P300 BCI is a vision-independent system because no visual interaction is required of the user. Specifically, we designed a direction-congruent bimodal paradigm by randomly and simultaneously presenting auditory and tactile stimuli from the same direction. Furthermore, the channels and number of trials were tailored to each user to improve online performance. With 12 participants, the average online information transfer rate (ITR) of the bimodal approach improved by 45.43% and 51.05% over that attained, respectively, with the auditory and tactile approaches individually. Importantly, the average online ITR of the bimodal approach, including the break time between selections, reached 10.77 bits/min. These findings suggest that the proposed bimodal system holds promise as a practical visual saccade-independent P300 BCI.

Disorders of consciousness (DOC) are among the major challenges of contemporary medicine, mostly due to the high rates of misdiagnoses in clinical assessment, based on behavioral scales. This turns our attention to potentially objective neuroimaging methods. Paradigms based on electroencephalography (EEG) are most suited for bedside applications, but sensitive to artifacts. These problems are especially pronounced in pediatric patients.

We present the first study on the assessment of pediatric DOC patients by means of command-following procedures and involving long-latency cognitive event-related potentials. To deal with the above mentioned challenges, we construct a specialized signal processing scheme including artifact correction and rejection, parametrization, classification and final assessment of the statistical significance. To compensate for the possible bias of the tests involved in the final diagnosis, we propose the Monte Carlo evaluation of the processing pipeline. To compensate for possible sensory impairments of DOC patients, for each subject we check command-following responses to the stimuli in the major modalities: visual, tactile, and audio (words and sounds).

We test the scheme on 20 healthy volunteers and present results for 15 patients from a hospital for children with severe brain damage, in relation to their behavioral diagnosis on the Coma Recovery Scale-Revised (CRS-R).

Carryover effects can contaminate ON/OFF BOLD contrasts designated in an fMRI experiment. Yet, the ON/OFF contrasts are essential to facilitate statistical analysis based on the significance of contrast levels. Here, we conducted an fMRI experiment with acupuncture stimulation applied on ST42 acupoint as well as with tactile stimulation on its skin surface. Experiment consisted of three two-block acupuncture and one two-block tactile fMRI runs. Each block started with 26-sec OFF period followed by either 26-sec needle manipulation in the acupuncture runs or by scratching skin surface with sand paper in the tactile. To test if carryover effects could alter the BOLD contrasts, we analyzed different portions of fMRI data using GLM method. Our results showed analyses on different portions of acupuncture fMRI data gave significantly different results. Statistical parametric maps of group random effects resulted from the analysis on the very first fMRI trial formed the broadest coverage of the active brain areas. BOLD model time course also best explained the adjusted raw time course at peak active voxel (coefficient of determination = 0.88). Analyses on other portions of fMRI data only selected subset of the active brain areas delineated by the analysis on the very first data trial and the BOLD model only mildly accounted for the adjusted raw time courses. In tactile runs, results were more consistent across analyses. Therefore, in fMRI experiments with strong carryover effects, a single-block experimental design with multiple repetitions, separated by long enough periods of time, should be more suitable to extract task BOLD effects.

This article focuses on the influence of walking speed and direction of the robot movement on the tactile ground classification. The perception system comprise force/torque sensor mounted on the foot of a six-legged robot. The force/torque signals are registered during the negotiation of several terrains. Next, based on the statistical or spectral analysis of the signal the robot is able to classify the terrain. In this paper we are concentrated on the influence of the walking speed and the direction of the robot movement on the classification performance. The results obtained proved that it is possible to learn the characteristics of the terrain using generalized classifier, which is trained on the dataset containing measurements acquired for the whole range of the selected gait parameter.