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Optimal sensor set for decoding motor imagery from EEG
Arnau Dillen, Fakhreddine Ghaffari, Olivier Romain, Bram Vanderborght, Uroš Marušič, Sidney Grosprêtre, Ann Nowé, Romain Meeusen, Kevin De Pauw, 2023, original scientific article

Abstract: Brain–computer interfaces (BCIs) have the potential to enable individuals to interact with devices by detecting their intention from brain activity. A common approach to BCI is to decode movement intention from motor imagery (MI), the mental representation of an overt action. However, research-grade electroencephalogram (EEG) acquisition devices with a high number of sensors are typically necessary to achieve the spatial resolution required for reliable analysis. This entails high monetary and computational costs that make these approaches impractical for everyday use. This study investigates the trade-off between accuracy and complexity when decoding MI from fewer EEG sensors. Data were acquired from 15 healthy participants performing MI with a 64-channel research-grade EEG device. After performing a quality assessment by identifying visually evoked potentials, several decoding pipelines were trained on these data using different subsets of electrode locations. No significant differences (p = [0.18–0.91]) in the average decoding accuracy were found when using a reduced number of sensors. Therefore, decoding MI from a limited number of sensors is feasible. Hence, using commercial sensor devices for this purpose should be attainable, reducing both monetary and computational costs for BCI control.
Keywords: brain-computer interface, motor imagery, feature reduction, electroencephalogram, machine learning
Published in DiRROS: 03.04.2023; Views: 330; Downloads: 130
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Stand up to excite the spine : neuromuscular, autonomic, and cardiometabolic responses during motor imagery in standing vs. sitting posture
Sidney Grosprêtre, Uroš Marušič, Philippe Gimenez, Gael Ennequin, Laurent Mourot, Laurie Isacco, 2021, original scientific article

Abstract: Motor imagery (MI) for health and performance strategies has gained interest in recent decades. Nevertheless, there are still no studies that have comprehensively investigated the physiological responses during MI, and no one questions the influence of low-level contraction on these responses. Thus, the aim of the present study was to investigate the neuromuscular, autonomic nervous system (ANS), and cardiometabolic changes associated with an acute bout of MI practice in sitting and standing condition. Twelve young healthy males (26.3 % 4.4 years) participated in two experimental sessions (control vs. MI) consisting of two postural conditions (sitting vs. standing). ANS, hemodynamic and respiratory parameters, body sway parameters, and electromyography activity were continuously recorded, while neuromuscular parameters were recorded on the right triceps surae muscles before and after performing the postural conditions. While MI showed no effect on ANS, the standing posture increased the indices of sympathetic system activity and decreased those of the parasympathetic system (p < 0.05). Moreover, MI during standing induced greater spinal excitability compared to sitting posture (p < 0.05), which was accompanied with greater oxygen consumption, energy expenditure, ventilation, and lower cardiac output (p < 0.05). Asking individuals to perform MI of an isometric contraction while standing allows them to mentally focus on the motor command, not challenge balance, and produce specific cardiometabolic responses. Therefore, these results provide further evidence of posture and MI-related modulation of spinal excitability with additional autonomic and cardiometabolic responses in healthy young men.
Keywords: heart rate, oxygen uptake, VO2, H-reflex, elektromyography
Published in DiRROS: 29.11.2021; Views: 640; Downloads: 625
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