Fatigue in children using motor imagery and P300 brain-computer interfaces.

BACKGROUND: Brain-computer interface (BCI) technology offers children with quadriplegic cerebral palsy unique opportunities for communication, environmental exploration, learning, and game play. Research in adults demonstrates a negative impact of fatigue on BCI enjoyment, while effects on BCI performance are variable. To date, there have been no pediatric studies of BCI fatigue. The purpose of this study was to assess the effects of two different BCI paradigms, motor imagery and visual P300, on the development of self-reported fatigue and an electroencephalography (EEG) biomarker of fatigue in typically developing children. METHODS: Thirty-seven typically-developing school-aged children were recruited to a prospective, crossover study. Participants attended three sessions: (A) motor imagery-BCI, (B) visual P300-BCI, and (C) video viewing (control). The motor imagery task involved an imagined left- or right-hand squeeze. The P300 task involved attending to one square on a 3 × 3 grid during a random single flash sequence. Each paradigm had respective calibration periods and a similar visual counting game. Primary outcomes were self-reported fatigue and the power of the EEG alpha band both collected during resting-state periods pre- and post-task. Self-reported fatigue was measured using a 10-point visual analog scale. EEG alpha band power was calculated as the integrated power spectral density from 8 to 12 Hz of the EEG spectrum. RESULTS: Thirty-two children completed the protocol (age range 7-16, 63% female). Self-reported fatigue and EEG alpha band power increased across all sessions (F(1,155) = 33.9, p < 0.001; F = 5.0(1,149), p = 0.027 respectively). No differences in fatigue development were observed between session types. There was no correlation between self-reported fatigue and EEG alpha band power change. BCI performance varied between participants and paradigms as expected but was not associated with self-reported fatigue or EEG alpha band power. CONCLUSION: Short periods (30-mintues) of BCI use can increase self-reported fatigue and EEG alpha band power to a similar degree in children performing motor imagery and P300 BCI paradigms. Performance was not associated with our measures of fatigue; the impact of fatigue on useability and enjoyment is unclear. Our results reflect the variability of fatigue and the BCI experience more broadly in children and warrant further investigation.

Beta, gamma band, and high-frequency coherence of EMGs of vasti muscles caused by clustering of motor units.

The vasti muscles stabilize the knee joint during the running movement. This requires some motor units to synchronize. Test the hypothesis that EMGs from the vasti muscles (VM and VL) are coherent in four frequency bands, one below 30 Hz, the 40 Hz (30-45 Hz), the middle band up to 120 Hz, and the high-frequency band (135-280 Hz). Because the VM during one step and the VL during another step contain common EMG signal parts the inter-step coherence at low frequencies does not disappear when the coherence is computed between the EMGVM obtained from one step and the EMGVL of the previous step. Twelve participants ran on a treadmill at 2.9 m/s for 15 min. EMGs were recorded from the vasti muscles using bipolar current amplifiers. Ordinary coherence was computed between the EMGVM and EMGVL and for the inter-step-condition. Significant coherence was observed in all frequency bands. In the mid- and high-frequency range, coherence disappears for the inter-step condition, whereas the low-frequency coherence is still present. Four frequency bands must be considered. It was proposed that coherence at low frequencies reflects cortico-muscular interactions. However, the clustering of motor unit action potentials is sufficient to generate the low-frequency coherence as well. There is a low-frequency coherence resulting from EMGs of the vasti muscles that are similar in different steps. Therefore, at least these three effects must be considered to draw conclusions from the coherence of the vasti muscles at low frequencies that occur while running.

A Current-based EEG Amplifier and Validation with a Saline Phantom and an SSVEP Paradigm.

Electroencephalography (EEG) measures the summed electrical activity from pyramidal cells in the brain by using noninvasive electrodes placed on the scalp. Traditional, voltage-based measurements are done with differential amplifiers. Depending on the location of the electrodes used for the differential measurement, EEG can estimate electrical activity from radially (common or average reference) or tangentially (bipolar derivation) oriented neurons. A limitation of the bipolar derivation is that when the electrodes are too close together, the conductive solution used to improve electrode-skin impedance can short-circuit the electrodes. Magnetoencephalography (MEG) also enables measurements from tangentially oriented cells without concerns about short-circuiting the electrodes. However, MEG is a more expensive, and a less available technology. Measuring from both radial and tangential cells can improve the resolution to localize the origin of brain activity; this could be extremely useful for diagnoses and treatment of several neurological disorders. The work presented here builds on previous research that aims to record the electrical activity of the tangentially oriented cells with technology like that of EEG. The design of the device presented here has been improved from previous implementations. Characterization of the electronics, and validation in a saline phantom and with a steady state visually evoked potentials paradigm is presented along with a comparison to a voltage-based (vEEG) amplifier. The current-based (cEEG) amplifier satisfies suggested parameters for EEG amplifiers, and exhibited higher sensitivity to tangential dipoles in the phantom study. It measured brain activity using the same scalp electrodes as vEEG amplifiers with comparable performance.

Development of a multichannel current-EMG system for coherence modulation with visual biofeedback.

By means of biofeedback, neuromotor control can be modified. Recent biofeedback experiments have used the power of the electromyogram of one muscle in different frequency bands to control a two-dimensional cursor. However, the human body usually requires coherent activation of multiple muscles to achieve daily life tasks. Additionally, electromyography (EMG) instrumentation has remained the same for decades, and might not be the most suitable to measure coherent activations from pennated muscles according to recent experiments by von Tscharner and colleagues. In this study, we propose the development of a multichannel current-based EMG amplifier to use intermuscular coherence as the control feature of a visual biofeedback system. The system was used in a leg extension protocol to voluntarily increase intermuscular coherence between the vastii muscles. Results from ten subjects show that it is possible to increase intermuscular coherence through visual biofeedback. Such a system can have applications in endurance training and rehabilitation.

A wavelet based time frequency analysis of electromyograms to group steps of runners into clusters that contain similar muscle activation patterns.

PURPOSE: To wavelet transform the electromyograms of the vastii muscles and generate wavelet intensity patterns (WIP) of runners. Test the hypotheses: 1) The WIP of the vastus medialis (VM) and vastus lateralis (VL) of one step are more similar than the WIPs of these two muscles, offset by one step. 2) The WIPs within one muscle differ by having maximal intensities in specific frequency bands and these intensities are not always occurring at the same time after heel strike. 3) The WIPs that were recorded form one muscle for all steps while running can be grouped into clusters with similar WIPs. It is expected that clusters might have distinctly different, cluster specific mean WIPs. METHODS: The EMG of the vastii muscles from at least 1000 steps from twelve runners were recorded using a bipolar current amplifier and yielded WIPs. Based on the weights obtained after a principal component analysis the dissimilarities (1-correlation) between the WIPs were computed. The dissimilarities were submitted to a hierarchical cluster analysis to search for groups of steps with similar WIPs. The clusters formed by random surrogate WIPs were used to determine whether the groups were likely to be created in a non-random manner. RESULTS: The steps were grouped in clusters showing similar WIPs. The grouping was based on the frequency bands and their timing showing that they represented defining parts of the WIPs. The correlations between the WIPs of the vastii muscles that were recorded during the same step were higher than the correlations of WPIs that were recorded during consecutive steps, indicating the non-randomness of the WIPs. CONCLUSIONS: The spectral power of EMGs while running varies during the stance phase in time and frequency, therefore a time averaged power spectrum cannot reflect the timing of events that occur while running. It seems likely that there might be a set of predefined patterns that are used upon demand to stabilize the movement.