Descomposición de bandas de energía como extracción de características para el reconocimiento de imaginación motora

En este artículo se presentan los resultados que se obtuvieron del análisis de las señales eléctricas, obtenidas con un equipo de interfaz cerebro-computadora, colocado en una persona que imagina movimientos de las extremidades superiores. En el análisis de la señal se aplica la descomposición de bandas de energía para la extracción de características a fin de detectar y clasificar las intenciones del usuario

Caracterización de imaginación motora utilizando análisis de descomposición de bandas de energía

This article provides an overview of the analysis of electrical signals generated by the brain, when a person imagines themovement of their upper extremities. The signals studied are acquired with a brain-computer interface equipment and analyzed using DiscreteWavelet transform as the energy band decomposition technique. The use of this technique is new in the analysis of electrical signals generated bythe brain and the results obtained indicate that the use of the technique is feasible for the characterization of electrical signals coming from thebrain.Este artículo trata sobre el análisis de las señales eléctricas generadas por el cerebro, cuando una persona imagina elmovimiento de sus extremidades superiores. Las señales que se estudian son adquiridas con un equipo de interfaz cerebro-computador y sonanalizadas utilizando la transformada Wavelet Discreta para la técnica de descomposición de banda de energía. El uso de esta técnica es novedosoen el análisis de las señales eléctricas generadas por el cerebro y los resultados que se obtuvieron indican que el uso de la técnica es factible parala caracterización de señales eléctricas provenientes del cerebro

MindTouch: Effect of Mindfulness Meditation on Mid-Air Tactile Perception

As we constantly seek to improve and expand upon the capabilities of technology, we frequently wonder whether we use technology to its fullest extent. Studies indicate that increasing our awareness and mindfulness of our senses may lead to a journey of unexplored experiences. In this paper, we focus on the perception of mid-air haptics stimuli and whether it can be improved through mindfulness meditation. We have conducted an experiment with 22 participants given the task to recognize digits 0 to 9 drawn on their palms using a mid-air haptic device under two conditions - with and without prior mindfulness meditation. Results show that for frequencies targeting both Meissner (40 Hz) and Pacinian (200 Hz) receptors, meditation significantly improves performance of the participants, as well as increases their confidence. This suggests that including a short meditation step in haptic user interfaces could lead to improved system performance and user satisfaction

Evoking Physiological Synchrony and Empathy Using Social VR with Biofeedback

With the advent of consumer grade virtual reality (VR) headsets and physiological measurement devices, new possibilities for mediated social interaction emerge enabling the immersion to environments where the visual features react to the users' physiological activation. In this study, we investigated whether and how individual and interpersonally shared biofeedback (visualised respiration rate and frontal asymmetry of electroencephalography, EEG) enhance synchrony between the users' physiological activity and perceived empathy towards the other during a compassion meditation exercise carried out in a social VR setting. The study was conducted as a laboratory experiment (N = 72) employing a Unity3D-based Dynecom immersive social meditation environment and two amplifiers to collect the psychophysiological signals for the biofeedback. The biofeedback on empathy-related EEG frontal asymmetry evoked higher self-reported empathy towards the other user than the biofeedback on respiratory activation, but the perceived empathy was highest when both feedbacks were simultaneously presented. In addition, the participants reported more empathy when there was stronger EEG frontal asymmetry synchronization between the users. The presented results inform the field of affective computing on the possibilities that VR offers for different applications of empathic technologies.Peer reviewe

Designing and Evaluating an Adaptive Virtual Reality System using EEG Frequencies to Balance Internal and External Attention States

Virtual reality finds various applications in productivity, entertainment, and training scenarios requiring working memory and attentional resources. Working memory relies on prioritizing relevant information and suppressing irrelevant information through internal attention, which is fundamental for successful task performance and training. Today, virtual reality systems do not account for the impact of working memory loads resulting in over or under-stimulation. In this work, we designed an adaptive system based on EEG correlates of external and internal attention to support working memory task performance. Here, participants engaged in a visual working memory N-Back task, and we adapted the visual complexity of distracting surrounding elements. Our study first demonstrated the feasibility of EEG frontal theta and parietal alpha frequency bands for dynamic visual complexity adjustments. Second, our adaptive system showed improved task performance and diminished perceived workload compared to a reverse adaptation. Our results show the effectiveness of the proposed adaptive system, allowing for the optimization of distracting elements in high-demanding conditions. Adaptive systems based on alpha and theta frequency bands allow for the regulation of attentional and executive resources to keep users engaged in a task without resulting in cognitive overload

Enhancing motor imagery detection efficacy using multisensory virtual reality priming

Brain-computer interfaces (BCI) have been developed to allow users to communicate with the external world by translating brain activity into control signals. Motor imagery (MI) has been a popular paradigm in BCI control where the user imagines movements of e.g., their left and right limbs and classifiers are then trained to detect such intent directly from electroencephalography (EEG) signals. For some users, however, it is difficult to elicit patterns in the EEG signal that can be detected with existing features and classifiers. As such, new user control strategies and training paradigms have been highly sought-after to help improve motor imagery performance. Virtual reality (VR) has emerged as one potential tool where improvements in user engagement and level of immersion have shown to improve BCI accuracy. Motor priming in VR, in turn, has shown to further enhance BCI accuracy. In this pilot study, we take the first steps to explore if multisensory VR motor priming, where haptic and olfactory stimuli are present, can improve motor imagery detection efficacy in terms of both improved accuracy and faster detection. Experiments with 10 participants equipped with a biosensor-embedded VR headset, an off-the-shelf scent diffusion device, and a haptic glove with force feedback showed that significant improvements in motor imagery detection could be achieved. Increased activity in the six common spatial pattern filters used were also observed and peak accuracy could be achieved with analysis windows that were 2 s shorter. Combined, the results suggest that multisensory motor priming prior to motor imagery could improve detection efficacy