Extraction of the Major Features of Brain Signals using Intelligent Networks

The brain-computer interface is considered one of the main tools for implementing and designing smart medical software. The analysis of brain signal data, called EEG, is one of the main tasks of smart medical diagnostic systems. While EEG signals have many components, one of the most important brain activities pursued is the P300 component. Detection of this component can help detect abnormalities and visualize the movement of organs of the body. In this research, a new method for processing EEG signals is proposed with the aim of detecting the P300 component. Major features were extracted from the BCI Competition IV EEG data set in a number of steps, i.e. normalization with the purpose of noise reduction using a median filter, feature extraction using a recurrent neural network, and classification using Twin Support Vector Machine. Then, a series of evaluation criteria were used to validate the proposed approach and compare it with similar methods. The results showed that the proposed approach has high accuracy

Noninvasive Neuroprosthetic Control of Grasping by Amputees

Smooth coordination and fine temporal control of muscles by the brain allows us to effortlessly pre-shape our hand to grasp different objects. Correlates of motor control for grasping have been found across wide-spread cortical areas, with diverse signal features. These signals have been harnessed by implanting intracortical electrodes and used to control the motion of robotic hands by tetraplegics, using algorithms called brain-machine interfaces (BMIs). Signatures of motor control signal encoding mechanisms of the brain in macro-scale signals such as electroencephalography (EEG) are unknown, and could potentially be used to develop noninvasive brain-machine interfaces. Here we show that a) low frequency (0.1 – 1 Hz) time domain EEG contains information about grasp pre-shaping in able-bodies individuals, and b) This information can be used to control pre-shaping motion of a robotic hand by amputees. In the first study, we recorded simultaneous EEG and hand kinematics as 5 able-bodies individuals grasped various objects. Linear decoders using low delta band EEG amplitudes accurately predicted hand pre-shaping kinematics during grasping. Correlation coefficient between predicted and actual kinematics was r = 0.59 ± 0.04, 0.47 ± 0.06 and 0.32 ± 0.05 for the first 3 synergies. In the second study, two transradial amputees (A1 and A2) controlled a prosthetic hand to grasp two objects using a closed-loop BMI with low delta band EEG. This study was conducted longitudinally in 12 sessions spread over 38 days. A1 achieved a 63% success rate, with 11 sessions significantly above chance. A2 achieved a 32% success rate, with 2 sessions significantly above chance. Previous methods of EEG-based BMIs used frequency domain features, and were thought to have a low signal-to-noise ratio making them unsuitable for control of dexterous tasks like grasping. Our results demonstrate that time-domain EEG contains information about grasp pre-shaping, which can be harnessed for neuroprosthetic control.Electrical and Computer Engineering, Department o

Avances en el control mental de una mano robótica

Introduction: The present article is the product of the research "Advances in the mental control of a robotic hand", developed at the University of Pamplona in the year 2019. Problem: Currently one of the main problems presented by robotic hand prostheses is the way in which the user indicates the movements to be performed. Given this, the best results have been obtained using invasive systems. Objective: The main objective of the system is to allow a person to control the movements and / or gestures of a robotic hand using their thoughts, in such a way that the control is as natural and precise as possible. Methodology: Use is made of a non-invasive, low-cost brain-computer interface (BCI) for the generation of control system references. Results: The performance of the system is directly subject to the user's ability to recreate actions or movements in their mind; the more defined your thinking, the better the control response. Conclusion: Mind control represents a new challenge for users, but as it is used, it becomes a more natural and precise control method, offering great control possibilities to people who make daily use of robotic hand prostheses. Originality: Through this research, an alternative is formulated for the control of hand prostheses, which does not require invasive systems and has the advantage of being low cost. Limitations: Frustration, stress and external noise are factors that directly affect the performance of the system

Comment on "On the Extraction of Purely Motor EEG Neural Correlates during an Upper Limb Visuomotor Task"

Bibian et al. show in their recent paper (Bibi\'an et al. 2021) that eye and head movements can affect the EEG-based classification in a reaching motor task. These movements can generate artefacts that can cause an overoptimistic estimation of the classification accuracy. They speculate that such artefacts jeopardise the interpretation of the results from several motor decoding studies including our study (Ofner et al. 2017). While we endorse their warning about artefacts in general, we do have doubts whether their work supports such a statement with respect to our study. We provide in this commentary a more nuanced contextualization of our work presented in Ofner et al. and the type of artefacts investigated in Bibian et al

Decoding of walking kinematics from non-invasively acquired electroencephalographic signals in stroke patients

Our group has recently shown the feasibility of decoding kinematics of controlled walking from the lower frequency range of electroencephalographic (EEG) signals during a precision walking task. Here, we turn our attention to stroke survivors who have had lesions resulting in hemiparetic gait. We recorded the EEG of stroke recovery patients during a precision treadmill walking task while tracking bilaterally the kinematics of the hips, knees, and ankles. In offline analyses, we applied a Wiener Filter and two unscented Kalman filters of 1st and 10th orders to predict estimates of the kinematic parameters from scalp EEG. Decoding accuracies from four patients who have had cortical and subcortical strokes were comparable with previous studies in healthy subjects. With improved decoding of EEG signals from damaged brains, we hope we can soon correlate activity to more intentional and normal-form walking that can guide users of a powered lower-body prosthetic or exoskeleton

EEG-Based Empathic Safe Cobot

An empathic collaborative robot (cobot) was realized through the transmission of fear from a human agent to a robot agent. Such empathy was induced through an electroencephalographic (EEG) sensor worn by the human agent, thus realizing an empathic safe brain-computer interface (BCI). The empathic safe cobot reacts to the fear and in turn transmits it to the human agent, forming a social circle of empathy and safety. A first randomized, controlled experiment involved two groups of 50 healthy subjects (100 total subjects) to measure the EEG signal in the presence or absence of a frightening event. The second randomized, controlled experiment on two groups of 50 different healthy subjects (100 total subjects) exposed the subjects to comfortable and uncomfortable movements of a collaborative robot (cobot) while the subjects’ EEG signal was acquired. The result was that a spike in the subject’s EEG signal was observed in the presence of uncomfortable movement. The questionnaires were distributed to the subjects, and confirmed the results of the EEG signal measurement. In a controlled laboratory setting, all experiments were found to be statistically significant. In the first experiment, the peak EEG signal measured just after the activating event was greater than the resting EEG signal (p < 10−3). In the second experiment, the peak EEG signal measured just after the uncomfortable movement of the cobot was greater than the EEG signal measured under conditions of comfortable movement of the cobot (p < 10−3). In conclusion, within the isolated and constrained experimental environment, the results were satisfactory