Search Results (48)

Sensor technology plays a fundamental role in neuro-motor rehabilitation by enabling precise movement analysis and control. This study explores the integration of brain–machine interfaces (BMIs) and wearable sensors to enhance motor recovery in individuals with neuro-motor impairments. Specifically, different time-frequency transforms are evaluated to analyze the correlation between electroencephalographic (EEG) activity and ankle position, measured by using inertial measurement units (IMUs). A low-cost ankle exoskeleton was designed to conduct the experimental trials. Six subjects performed plantar and dorsal flexion movements while the EEG and IMU signals were recorded. The correlation between brain activity and foot kinematics was analyzed using the Short-Time Fourier Transform (STFT), Stockwell (ST), Hilbert–Huang (HHT), and Chirplet (CT) methods. The 8–20 Hz frequency band exhibited the highest correlation values. For motor imagery classification, the STFT achieved the highest accuracy (92.9%) using an EEGNet-based classifier and a state-machine approach. This study presents a dual approach: the analysis of EEG-movement correlation in different cognitive states, and the systematic comparison of four time-frequency transforms for both correlation and classification performance. The results support the potential of combining EEG and IMU data for BMI applications and highlight the importance of cognitive state in motion analysis for accessible neurorehabilitation technologies. Full article

Current technological solutions for Brain-machine Interfaces (BMI) achieve reasonable accuracy, but most systems are large in size, power consuming and not auto-adaptive. This work addresses the question whether current neuromorphic technologies could resolve these problems? The paper proposes a novel neuromorphic framework of a BMI system for prosthetics control via decoding Electro Cortico-Graphic (ECoG) brain signals. It includes a three-dimensional spike timing neural network (3D-SNN) for brain signals features extraction and an on-line trainable recurrent reservoir structure (Echo state network (ESN)) for Motor Control Decoding (MCD). A software system, written in Python using NEST Simulator SNN library is described. It is able to adapt continuously in real time in supervised or unsupervised mode. The proposed approach was tested on several experimental data sets acquired from a tetraplegic person. First simulation results are encouraging, showing also the need for a further improvement via multiple hyper-parameters tuning. Its future implementation on a neuromorphic hardware platform that is smaller in size and significantly less power consuming is discussed too. Full article

The motor cortex of non-human primates plays a key role in brain–machine interface (BMI) research. In addition to recording cortical neural signals, accurately and efficiently capturing the hand movements of experimental animals under unconstrained conditions remains a key challenge. Addressing this challenge can deepen our understanding and application of BMI behavior from both theoretical and practical perspectives. To address this issue, we developed a deep learning framework that combines Yolov5 and RexNet-ECA to reliably detect the hand joint positions of freely moving primates at different distances using a single camera. The model simplifies the setup procedure while maintaining high accuracy, with an average keypoint detection error of less than three pixels. Our method eliminates the need for physical markers, ensuring non-invasive data collection while preserving the natural behavior of the experimental subjects. The proposed system exhibits high accuracy and ease of use compared to existing methods. By quickly and accurately acquiring spatiotemporal behavioral metrics, the method provides valuable insights into the dynamic interplay between neural and motor functions, further advancing BMI research. Full article

Background: Democratized access to safe and effective robotic neurorehabilitation for stroke survivors requires innovative, affordable solutions that can be used not only in clinics but also at home. This requires the high usability of the devices involved to minimize costs associated with support from physical therapists or technicians. Methods: This paper describes the early findings of the NeuroExo brain–machine interface (BMI) with an upper-limb robotic exoskeleton for stroke neurorehabilitation. This early feasibility study consisted of a six-week protocol, with an initial training and BMI calibration phase at the clinic followed by 60 sessions of neuromotor therapy at the homes of the participants. Pre- and post-assessments were used to assess users’ compliance and system performance. Results: Participants achieved a compliance rate between $21\%$ and $100\%$, with an average of $69\%$, while maintaining adequate signal quality and a positive perceived BMI performance during home usage with an average Likert scale score of four out of five. Moreover, adequate signal quality was maintained for four out of five participants throughout the protocol. These findings provide valuable insights into essential components for comprehensive rehabilitation therapy for stroke survivors. Furthermore, linear mixed-effects statistical models showed a significant reduction in trial duration (p-value < 0.02) and concomitant changes in brain patterns (p-value < 0.02). Conclusions: the analysis of these findings suggests that a low-cost, safe, simple-to-use BMI system for at-home stroke rehabilitation is feasible. Full article

This systematic review explores the potential of the gamified brain–machine interfaces (BMIs) and brain–computer interfaces (BCIs) to enhance the quality of life for individuals with disabilities. These technologies promise to solve complex problems by delivering customized interventions considering individual needs, ethical dilemmas, and practical constraints. This review follows the PRISMA statement. The search process extensively explored multiple registered databases for studies published between 2015 and 2024. Articles were selected based on strict eligibility criteria, focusing on empirical research evaluating gamified BCIs and BMIs in rehabilitation and learning. The final analysis included 56 studies. A thorough examination emphasizes the transformative potential of gamified BCIs and BMIs for people with disabilities, highlighting the need for interdisciplinary collaboration, user-centered design principles, and ethical consciousness for gamified neurotechnology. These technologies mark a significant change by providing enjoyable and effective treatments for disabled individuals. It also delves into how gamification, neurofeedback, and adaptive learning techniques can enhance motivation, engagement, and overall well-being. This evaluation underscores the efficiency of gamified BCIs and BMIs as potential instruments for improving the quality of life and empowering disabled people. However, despite their apparent potential for rehabilitation and learning, more research is needed to validate their effectiveness, accessibility, and long-term benefits. Full article

Brain–computer interfaces, where motor imagery electroencephalography (EEG) signals are transformed into control commands, offer a promising solution for enhancing the standard of living for disabled individuals. However, the performance of EEG classification has been limited in most studies due to a lack of attention to the complementary information inherent at different temporal scales. Additionally, significant inter-subject variability in sensitivity to biological motion poses another critical challenge in achieving accurate EEG classification in a subject-dependent manner. To address these challenges, we propose a novel machine learning framework combining multi-scale feature fusion, which captures global and local spatial information from different-sized EEG segmentations, and adaptive Lasso-based feature selection, a mechanism for adaptively retaining informative subject-dependent features and discarding irrelevant ones. Experimental results on multiple public benchmark datasets revealed substantial improvements in EEG classification, achieving rates of 81.36%, 75.90%, and 68.30% for the BCIC-IV-2a, SMR-BCI, and OpenBMI datasets, respectively. These results not only surpassed existing methodologies but also underscored the effectiveness of our approach in overcoming specific challenges in EEG classification. Ablation studies further confirmed the efficacy of both the multi-scale feature analysis and adaptive selection mechanisms. This framework marks a significant advancement in the decoding of motor imagery EEG signals, positioning it for practical applications in real-world BCIs. Full article

Background. Brain–machine interfaces (BMIs) offer users the ability to directly communicate with digital devices through neural signals decoded with machine learning (ML)-based algorithms. Spiking Neural Networks (SNNs) are a type of Artificial Neural Network (ANN) that operate on neural spikes instead of continuous scalar outputs. Compared to traditional ANNs, SNNs perform fewer computations, use less memory, and mimic biological neurons better. However, SNNs only retain information for short durations, limiting their ability to capture long-term dependencies in time-variant data. Here, we propose a novel spike-weighted SNN with spiking long short-term memory (swSNN-SLSTM) for a regression problem. Spike-weighting captures neuronal firing rate instead of membrane potential, and the SLSTM layer captures long-term dependencies. Methods. We compared the performance of various ML algorithms during decoding directional movements, using a dataset of microelectrode recordings from a macaque during a directional joystick task, and also an open-source dataset. We thus quantified how swSNN-SLSTM performed compared to existing ML models: an unscented Kalman filter, LSTM-based ANN, and membrane-based SNN techniques. Result. The proposed swSNN-SLSTM outperforms both the unscented Kalman filter, the LSTM-based ANN, and the membrane based SNN technique. This shows that incorporating SLSTM can better capture long-term dependencies within neural data. Also, our proposed swSNN-SLSTM algorithm shows promise in reducing power consumption and lowering heat dissipation in implanted BMIs. Full article

The study of brainwaves and brain–computer interfaces (BCIs) or brain–machine interfaces (BMIs) has emerged as a transformative field with the potential to revolutionize society’s well-being. This technical paper delves into the multifaceted domain of brainwave analysis and its integration with BCIs, presenting an approach that aims to enhance the fabric of society through various applications, with BCIs aiding in various assistive technologies, the detection of neurological abnormalities, and biofeedback mechanisms for improved concentration. This study explores the relationship between brainwave patterns and the levels of focus using EEG data. The results reveal distinct changes in brainwave activity, notably in the delta and beta frequency ranges, corresponding to different levels of cognitive engagement. Building upon these findings, we propose the development of a biofeedback-based concentration enhancement program for students. This study, using an approach equipped with real-time EEG monitoring and feedback mechanisms, aims to empower students to improve their concentration, particularly in educational settings. Such an innovative approach holds promise for enhancing academic performance and learning experiences, offering valuable insights into the optimization of cognitive functions through neurofeedback interventions. Full article

This study presents a human-computer interaction combined with a brain-machine interface (BMI) and obstacle detection system for remote control of a wheeled robot through movement imagery, providing a potential solution for individuals facing challenges with conventional vehicle operation. The primary focus of this work is the classification of surface EEG signals related to mental activity when envisioning movement and deep relaxation states. Additionally, this work presents a system for obstacle detection based on image processing. The implemented system constitutes a complementary part of the interface. The main contributions of this work include the proposal of a modified 10–20-electrode setup suitable for motor imagery classification, the design of two convolutional neural network (CNNs) models employed to classify signals acquired from sixteen EEG channels, and the implementation of an obstacle detection system based on computer vision integrated with a brain-machine interface. The models developed in this study achieved an accuracy of 83% in classifying EEG signals. The resulting classification outcomes were subsequently utilized to control the movement of a mobile robot. Experimental trials conducted on a designated test track demonstrated real-time control of the robot. The findings indicate the feasibility of integration of the obstacle detection system for collision avoidance with the classification of motor imagery for the purpose of brain-machine interface control of vehicles. The elaborated solution could help paralyzed patients to safely control a wheelchair through EEG and effectively prevent unintended vehicle movements. Full article

In brain–machine interface (BMI) systems, the performance of trained Convolutional Neural Networks (CNNs) is significantly influenced by the quality of the training data. Another issue is the training time of CNNs. This paper introduces a novel approach by combining transfer learning and a Genetic Algorithm (GA) to optimize the training data of CNNs. Transfer learning is implemented across different subjects, and the data chosen by GA aim to improve CNN performance. In addition, the GA-selected data shed light on the similarity in brain activity between subjects. Two datasets are used: (1) the publicly available BCI Competition IV, in which the subjects performed motor imagery (MI) tasks, and (2) the dataset created by healthy subjects of our laboratory performing motor movement (MO) tasks. The experimental results indicate that the brain data selected by the GA improve the recognition accuracy of the target CNN (TCNN) using pre-trained base CNN (BCNN). The improvement in accuracy is 11% and 4% for the BCI Competition IV and our laboratory datasets, respectively. In addition, the GA-selected training data reduce the CNN training time. The performance of the trained CNN, utilizing transfer learning, is tested for real-time control of a robot manipulator. Full article

The sense of embodiment (SoE) is an essential element of human perception that allows individuals to control and perceive the movements of their body parts. Brain–machine interface (BMI) technology can induce SoE in real time, and adding sensory feedback through various modalities has been shown to improve BMI control and elicit SoEe. In this study, we conducted a systematic review to study BMI performance in studies that integrated SoE variables and analyzed the contribution of single or multimodal sensory stimulation. Out of 493 results, only 20 studies analyzed the SoE of humans using BMIs. Analysis of these articles revealed that 40% of the studies relating BMIs with sensory stimulation and SoE primarily focused on manipulating visual stimuli, particularly in terms of coherence (i.e., synchronous vs. asynchronous stimuli) and realism (i.e., humanoid or robotic appearance). However, no study has analyzed the independent contributions of different sensory modalities to SoE and BMI performance. These results suggest that providing a detailed description of the outcomes resulting from independent and combined effects of different sensory modalities on the experience of SoE during BMI control may be relevant for the design of neurorehabilitation programs. Full article

The article presents an overview of several studies in the field of Brain–Computer Interfaces (BCIs), the requirements for the architecture of such promising devices, as well as multi-modal BCI for drone control in a smart-city environment. Distinctive features of the proposed solution are the simplicity of the architecture (the use of only one smartphone for both receiving and processing bio-signals from the headset and transmitting commands to the drone), an open-source software solution for signal processing, generating, and sending commands to the unmanned aerial vehicle (UAV), as well as multimodality of the BCI (the use of both electroencephalographic (EEG) and electrooculographic (EOG) signals of the operator). For bio-signal acquisition, we used the NeuroSky Mindwave Mobile 2 headset, which is connected to an Android-based smartphone via Bluetooth. The developed Android application (Tello NeuroSky) processes signals from the headset and generates and transmits commands to the DJI Tello UAV via Wi-Fi. The decrease (depression) and increase of $\alpha$- and $\beta$-rhythms of the brain, as well as EOG signals that occur during blinking were the triggers for UAV commands. The developed software allows the manual setting of the minimum, maximum and threshold values for the processed bio-signals. The following commands for the UAV were implemented: take-off, landing, forward movement, and backwards movement. Two threads of the smartphone’s central processing unit (CPU) were utilized when processing signals in the software to increase the performance: for signal processing (1-D Daubechies 2 (db2) wavelet transform) and updating data on the diagrams, and for generating and transmitting commands to the drone. Full article

Brain–Machine Interfaces (BMIs) have made significant progress in recent years; however, there are still several application areas in which improvement is needed, including the accurate prediction of body movement during Virtual Reality (VR) simulations. To achieve a high level of immersion in VR sessions, it is important to have bidirectional interaction, which is typically achieved through the use of movement-tracking devices, such as controllers and body sensors. However, it may be possible to eliminate the need for these external tracking devices by directly acquiring movement information from the motor cortex via electroencephalography (EEG) recordings. This could potentially lead to more seamless and immersive VR experiences. There have been numerous studies that have investigated EEG recordings during movement. While the majority of these studies have focused on movement prediction based on brain signals, a smaller number of them have focused on how to utilize them during VR simulations. This suggests that there is still a need for further research in this area in order to fully understand the potential for using EEG to predict movement in VR simulations. We propose two neural network decoders designed to predict pre-arm-movement and during-arm-movement behavior based on brain activity recorded during the execution of VR simulation tasks in this research. For both decoders, we employ a Long Short-Term Memory model. The study’s findings are highly encouraging, lending credence to the premise that this technology has the ability to replace external tracking devices. Full article

Biometric identification uses person recognition techniques based on the extraction of some of their physical or biological properties, which make it possible to characterize and differentiate one person from another and provide irreplaceable and critical information that is suitable for application in security systems. The extraction of information from the electrical biosignal of the human brain has received a great deal of attention in recent years. Analysis of EEG signals has been widely used over the last century in medicine and as a basis for brain–machine interfaces (BMIs). In addition, the application of EEG signals for biometric recognition has recently been demonstrated. In this context, EEG-based biometric systems are often considered in two different applications: identification (one-to-many classification) and authentication (one-to-one or true/false classification). In this article, we establish a methodology for selecting and reducing the minimum number of EEG sensors necessary to carry out effective biometric identification of individuals. Two methodologies were applied, one based on principal component analysis and the other on the Wilcoxon signed-rank test in order to reduce the number of electrodes. This allowed us to identify, according to the methodology used, the areas of the cerebral cortex that would allow selection of the minimum number of electrodes necessary for the identification of individuals. The methodologies were applied to two databases, one with 13 people with self-collected recordings using low-cost EEG equipment, EMOTIV EPOC+, and another publicly available database with recordings from 109 people provided by the PhysioNet BCI. Full article

The aging process is a multifaceted phenomenon that affects cognitive-affective and physical functioning as well as interactions with the environment. Although subjective cognitive decline may be part of normal aging, negative changes objectified as cognitive impairment are present in neurocognitive disorders and functional abilities are most impaired in patients with dementia. Electroencephalography-based brain–machine interfaces (BMI) are being used to assist older people in their daily activities and to improve their quality of life with neuro-rehabilitative applications. This paper provides an overview of BMI used to assist older adults. Both technical issues (detection of signals, extraction of features, classification) and application-related aspects with respect to the users’ needs are considered. Full article