Low-cost Brain-computer Interface Research Articles

Architectural Proposal for Low-Cost Brain–Computer Interfaces with ROS Systems for the Control of Robotic Arms in Autonomous Wheelchairs

Neurodegenerative diseases present significant challenges in terms of mobility and autonomy for patients. In the current context of technological advances, brain–computer interfaces (BCIs) emerge as a promising tool to improve the quality of life of these patients. Therefore, in this study, we explore the feasibility of using low-cost commercial EEG headsets, such as Neurosky and Brainlink, for the control of robotic arms integrated into autonomous wheelchairs. These headbands, which offer attention and meditation values, have been adapted to provide intuitive control based on the eight EEG signal values read from Delta to Gamma (high and low/medium Gamma) collected from the users’ prefrontal area, using only two non-invasive electrodes. To ensure precise and adaptive control, we have incorporated a neural network that interprets these values in real time so that the response of the robotic arm matches the user’s intentions. The results suggest that this combination of BCIs, robotics, and machine learning techniques, such as neural networks, is not only technically feasible but also has the potential to radically transform the interaction of patients with neurodegenerative diseases with their environment.

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare.

The present work focuses on how to build a wearable brain-computer interface (BCI). BCIs are a novel means of human-computer interaction that relies on direct measurements of brain signals to assist both people with disabilities and those who are able-bodied. Application examples include robotic control, industrial inspection, and neurorehabilitation. Notably, recent studies have shown that steady-state visually evoked potentials (SSVEPs) are particularly suited for communication and control applications, and efforts are currently being made to bring BCI technology into daily life. To achieve this aim, the final system must rely on wearable, portable, and low-cost instrumentation. In exploiting SSVEPs, a flickering visual stimulus with fixed frequencies is required. Thus, in considering daily-life constraints, the possibility to provide visual stimuli by means of smart glasses was explored in this study. Moreover, to detect the elicited potentials, a commercial device for electroencephalography (EEG) was considered. This consists of a single differential channel with dry electrodes (no conductive gel), thus achieving the utmost wearability and portability. In such a BCI, the user can interact with the smart glasses by merely staring at icons appearing on the display. Upon this simple principle, a user-friendly, low-cost BCI was built by integrating extended reality (XR) glasses with a commercially available EEG device. The functionality of this wearable XR-BCI was examined with an experimental campaign involving 20 subjects. The classification accuracy was between 80%-95% on average depending on the stimulation time. Given these results, the system can be used as a human-machine interface for industrial inspection but also for rehabilitation in ADHD and autism.

DeepBrain

With the recent advancements of electroencephalograph (EEG) techniques, some brain-computer interface (BCI) solutions have been explored to assist individuals performing various tasks with their minds. One promising application is to combine BCI with robotic systems so that the mobility-impaired people can control robots to take care of themselves. Towards this ultimate goal to design BCIs for mobility-impaired, we firstly conducted an online survey with 54 mobility-impaired participants who barely had previous experience with BCI to identify the challenges they face in life for the purpose of designing a personalized BCI system in need. The results revealed these challenges including small daily tasks (such as feeding and cleaning), which weigh on the financial burdens of hiring a caregiver. Meanwhile, the off-the-shelf high-fidelity BCIs are often expensive, whereas the cheaper devices only collect coarse-grained signals, preventing practical application in care aids due to lack of temporal resolution and accuracy. Based on the survey findings, we then designed DeepBrain, a human-centered learning augmented BCI system, that requires only coarse-grained brain signals with low-cost BCI equipment, but supports fine-grained brain-robot interaction and scalable multi-robot collaboration for domestic multi-task operations. A follow-up system comparison with other approaches show that the proposed human-centered solution is a promising step towards the ultimate goal, as it achieves satisfactory accuracy with less low computation resources. Also the practical brain to multi-robot interaction system validates the feasibility of our framework and model used in DeepBrain.

Mind Games: A Web-Based Multiplayer Brain-Computer Interface Game

The advancement of consumer-grade Brain-Computer Interface (BCI) devices has led to the use of brain wave data in disciplines beyond neuroscience. While BCI devices are studied holistically in some domains, the potential remains unexplored in other applicable domains, such as gaming. The majority of BCI games are single-player, only work on a single platform, or require expensive hardware that is difficult to use. To address these challenges, we propose Mind Games: a competitive multiplayer tug-of-war game played entirely with brain wave data in a web browser with a low-cost consumer-grade BCI device. Unlike other BCI games, Mind Games is platform-independent, has a quick setup time, and uses the latest web-based technologies: web-Bluetooth and JavaScript frameworks for networking and signal processing. This new cross-platform BCI pipeline enables physiological computing applications to be more accessible, leading the future of online multiplayer BCI games.

Emotion Classification from EEG with a Low-Cost BCI Versus a High-End Equipment.

The assessment of physiological signals such as the electroencephalography (EEG) has become a key point in the research area of emotion detection. This study compares the performance of two EEG devices, a low-cost brain-computer interface (BCI) (Emotiv EPOC+) and a high-end EEG (BrainVision), for the detection of four emotional conditions over 20 participants. For that purpose, signals were acquired with both devices under the same experimental procedure, and a comparison was made under three different scenarios, according to the number of channels selected and the sampling frequency of the signals analyzed. A total of 16 statistical, spectral and entropy features were extracted from the EEG recordings. A statistical analysis revealed a major number of statistically significant features for the high-end EEG than the BCI device under the three comparative scenarios. In addition, different machine learning algorithms were used for evaluating the classification performance of the features extracted from high-end EEG and low-cost BCI in each scenario. Artificial neural networks reported the best performance for both devices with an F[Formula: see text]-score of 75.08% for BCI and 98.78% for EEG. Although the professional EEG outcomes were higher than the low-cost BCI ones, both devices demonstrated a notable performance for the classification of the four emotional conditions.

Low-cost brain computer interface for everyday use

With the growth in electroencephalography (EEG) based applications the demand for affordable consumer solutions is increasing. Here we describe a compact, low-cost EEG device suitable for daily use. The data are transferred from the device to a personal server using the TCP-IP protocol, allowing for wireless operation and a decent range of motion for the user. The device is compact, having a circular shape with a radius of only 25mm, which would allow for comfortable daily use during both daytime and nighttime. Our solution is also very cost effective, approximately $350 for 24 electrodes. The built-in noise suppression capability improves the accuracy of recordings with a peak input noise below 0.35μV. Here, we provide the results of the tests for the developed device. On our GitHub page, we provide detailed specification of the steps involved in building this EEG device which should be helpful to readers designing similar devices for their needs https://github.com/Ildaron/ironbci .

SSVEP-based active control of an upper limb exoskeleton using a low-cost brain–computer interface

PurposeFor the robot-assisted upper limb rehabilitation training process of the elderly with damaged neuromuscular channels and hemiplegic patients, bioelectric signals are added to transform the traditional passive training mode into the active training mode.Design/methodology/approachThis paper mainly builds a steady-state visual stimulation interface, an electroencephalography (EEG) signal processing platform and an exoskeleton robot verification platform. The target flashing stimulation blocks provide visual stimulation at the specified position according to the specified frequency and stimulate EEG signals of different frequency bands. The EEG signal-processing platform constructed in this paper removes the noise by using Butterworth band-pass filtering and common average reference filtering on the obtained signals. Further, the features are extracted to identify the volunteer’s active movement intention through the canonical correlation analysis (CCA) method. The classification results are transmitted to the upper limb exoskeleton robot control system, combined with the position and posture of the exoskeleton robot to control the joint motion of robot.FindingsThrough a large number of experimental studies, the average accuracy of offline recognition of motion intention recognition can reach 86.1%. The control strategy with a three-instruction judgment method reduces the average execution error rate of the entire control system to 6.75%. Online experiments verify the feasibility of the steady-state visual evoked potentials (SSVEP)-based rehabilitation system.Originality/valueAn EEG signal analysis method based on SSVEP is integrated into the control of an upper limb exoskeleton robot, transforming the traditional passive training mode into the active training mode. The device used to record EEG is of very low cost, which has the potential to promote the rehabilitation system for further widely applications.

Towards the Development of a Brain Semi-controlled Wheelchair for Navigation in Indoor Environments

Several technological advancements emerged providing the technical assistance supporting people with special needs in tackling their everyday tasks. Particularly, with the advancements in cost-effective Brain-Computer Interfaces (BCI), they can be very useful for people with disabilities to improve their quality of life. This paper investigates the usability of low-cost BCI for navigation in an indoor environment, which is considered one of the daily challenges facing individuals with mobility impairment. A software framework is proposed to control a wheelchair using three modes of operations: brain-controlled, autonomous and semi-autonomous, taking into consideration the usability and safety requirements. A prototype system based on the proposed framework was developed. The system can detect an obstacle in the front, right and left sides of the wheelchair and can stop the movement automatically to avoid collation. The usability evaluation of the proposed system, in terms of effectiveness, efficiency and satisfaction, shows that it can be very helpful in the daily life of the mobility impaired people. An experiment was conducted to assess the usability of the proposed framework using the prototype system. Subjects steered the wheelchair using the three different operation modes effectively by controlling the direction of motion.

A Low-Cost Real-Time BCI Integration for Automated Door Opening System

Based on the ElectroEncephaloGraphy (EEG) signals obtained by noninvasive methods, Brain–Computer Interface (BCI) technologies provide real-time communication of individuals within the cyber-physical systems. However, this research area still needs low-cost solutions to become a research topic widely all around the world. In this paper, a low cost and productive BCI equipment was developed to assist daily living activities for disabled people. The control application of an integrated approach aims to open and close a door equipped with an EEG-based lock system using eye blink signals is developed. The proposed easy-to-use, portable BCI system includes cost-effective hardware/software components, modular and flexible configuration features and Bluetooth-enabled real-time data processing units. The developed BCI hardware is based on Texas Instruments ADS1299 AFE ADC, which features high flexibly in configuring of measure of various biopotentials from the human body using designed active/passive dry electrodes. Besides it is equipped with a built-in real-time data storage unit and accelerometer to make an online diagnosis, and data fusion at the interested signal investigation. Furthermore, desktop software has been implemented in the QT SDK environment, which can integrate remote BCI hardware during configuration. Experimental results of the designed system were compared with the results of the commercial-grade counterpart.

Advances in the mental control of a robotic hand

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.

Electroencephalographic spectral analysis from a wireless low-cost brain-computer interface for symptom capture of auditory verbal hallucinations in schizophrenia

Electroencephalographic spectral analysis from a wireless low-cost brain-computer interface for symptom capture of auditory verbal hallucinations in schizophrenia

MILA: Low-cost BCI framework for acquiring EEG data with IoT

The brain is a vital organ in the human body that acts as the center of the human nervous system. Brain-computer interface (BCI) uses electroencephalography (EEG) signals as information on brain activity. Hospitals usually use EEG as a diagnosis of brain disease. Combining EEG as part of IoT (Internet of Things) with high mobility is challenging research. This research tries to make a low-cost BCI framework for motorcycle riders. Analysis of brain activity from EEG data when motorcycle riders turn left or turn right. Therefore, the method of further installation must produce the right features to obtain precise and accurate brainwave characteristics from EEG signals. This research uses the concept of IoT with software engineering to recording human brain waves so that it becomes a practical device for the wearer. The purpose of this study is to create a low-cost BCI framework for obtaining EEG data.

Tractor Assistant Driving Control Method Based on EEG Combined With RNN-TL Deep Learning Algorithm

Nowadays, fieldwork is often accompanied by tight schedules, which tends to strain the shoulder muscles due to high-intensity work. Moreover, it is difficult and stressful for the disabled to drive agricultural machinery. Besides, current artificial intelligence technology could not fully realize tractor autonomous driving because of a high uncertain filed environment and short interruptions of satellite navigation signal shaded by trees. To reduce manual operations, a tractor assistant driving control method was proposed based on the human-machine interface utilizing the electroencephalographic (EEG) signal. First, the EEG signals of the tractor drivers were collected by a low-cost brain-computer interface (BCI), followed by denoising using a wavelet packet. Then the spectral features of EEG signals were calculated and extracted as the input of Recurrent Neural Network (RNN). Finally, the EEG-aided RNN driving model was trained for tractor driving robot control such as straight going, brake, left turn, and right turn operations, which control accuracy was 94.5% and time cost was 0.61 ms. Also, 8 electrodes were selected by the PCA algorithm for the design of a portable EEG controller. And the control accuracy reached 93.1% with the time cost of 0.48 ms. To solve the incomplete driving data set in the actual world because some driving manners may cause dangerous or even death, RNN-TL algorithm was employed by creating the complete driving data in the virtual environment followed by transferring the driving control experience to the actual world with small actual driving data set in the field, which control accuracy was 93.5% and time consumption was 0.48 ms. The experimental results showed the feasibility of the proposed tractor driving control method based on EEG signal combined with RNN-TL deep learning algorithm which can work with the displacement error less than 6.7 mm when the tractor speed is less than 50 km/h.

High-Speed Visual Target Identification for Low-Cost Wearable Brain-Computer Interfaces

Non-invasive brain-computer interfaces (BCI) have received a great deal of attention due to recent advances in signal processing. Two types of electroencephalograms (EEG), P300 and steady-state visual evoked potential (SSVEP), have been widely used to enable paralyzed patients to communicate with others. Although there have been many signal processing algorithms focusing on target identification accuracies such as power spectral density analysis (PSDA) and canonical correlation analysis (CCA), their high computational complexity drives up the cost of such systems. In the proposed lightweight target identification algorithm, we have focused on developing an improved information transfer rate (ITR) for high-quality communication and reducing overall implementation cost. The proposed algorithm, CCA-Lite, includes two algorithmic optimizations–signal binarization and on-the-fly covariance matrix calculation–which have enabled the development of a low-cost, single-channel, and wearable BCI system using SSVEP. The prototypical BCI system makes use of an ARM Cortex-M3-based low-cost microcontroller unit (MCU), which has been built for 1.5s SSVEP recordings. Compared to the state-of-the-art CCA-based target identification algorithm, CCA-Lite exhibits 25% better ITR and has reduced memory requirements by 92% and single-target identification cycle time by 26%.

Reactive frequency band-based real-time motor imagery classification

The requirement of an effective online processing algorithm becomes very vital to fulfilling the demand of the low-cost brain–computer interface (BCI) system. The authors proposed a very first and robust unsupervised machine learning algorithm, for the real-time classification of movement imagination. The reactive frequency band (RFB) of the individual subject has been identified through the dominant frequency detection algorithm over the training dataset. Based on the identified RFB, the feature extraction process has been applied to the testing dataset. The estimated 'feature' further classified as per probabilistic Bayesian classifier. The effectiveness of the proposed RFB detection method of electroencephalogram (EEG) signal is validated by self-generated artificial sine wave signal, single subject and nine subject movement imagery (MI) BCI competition dataset. The proposed method of EEG signal processing outperformed the conventional wavelet-based BCI competition II results and the wavelet-based algorithm applied over the BCI competition IV dataset.

Reactive frequency band-based real-time motor imagery classification

The requirement of an effective online processing algorithm becomes very vital to fulfilling the demand of the low-cost brain–computer interface (BCI) system. The authors proposed a very first and robust unsupervised machine learning algorithm, for the real-time classification of movement imagination. The reactive frequency band (RFB) of the individual subject has been identified through the dominant frequency detection algorithm over the training dataset. Based on the identified RFB, the feature extraction process has been applied to the testing dataset. The estimated 'feature' further classified as per probabilistic Bayesian classifier. The effectiveness of the proposed RFB detection method of electroencephalogram (EEG) signal is validated by self-generated artificial sine wave signal, single subject and nine subject movement imagery (MI) BCI competition dataset. The proposed method of EEG signal processing outperformed the conventional wavelet-based BCI competition II results and the wavelet-based algorithm applied over the BCI competition IV dataset.

Spectral Subtraction Denoising Preprocessing Block to Improve Slow Cortical Potential Based Brain–Computer Interface

The measured electroencephalographic (EEG) signals used in brain–computer interface (BCI) are usually contaminated with several additive random and physiological noise components. Even though several preprocessing strategies were proposed in the literature to extract useful EEG signal components for further processing and analysis, their performance is yet to meet practical needs to boost BCI applications performance. Of particular interest among those methods are the ones addressing random noise suppression that could help enhance the performance of low-cost BCI systems/headsets. A preprocessing block for signal denoising of slow cortical potential (SCP) that provides efficient noise removal and better classification accuracy is presented. This method is a variant of the spectral subtraction signal denoising whereby the noise power spectrum is estimated and removed adaptively. This method is applied to each EEG channel separately thus providing a preprocessing block that could be integrated with the existing spatial/temporal preprocessing methods. The impact of this method on classification accuracy is studied and compared to the conventional wavelet shrinkage method. The proposed method is verified using experimental data from the BCI competition II data set whereby a performance boost was obtained with the new preprocessing block. The improvement was quantitatively assessed using mean square error and mean absolute error measures and was also shown to be statistically significant. Moreover, spectral subtraction denoising is shown to have less computational complexity than wavelet shrinkage based methods. The new preprocessing block for SCP-based BCI signals based on spectral subtraction provides significant improvement in performance when added and offers an adaptive yet less computationally expensive alternative to existing methods such as wavelet shrinkage based denoising method. Also, given its independent channel processing, it shows potential for seamless integration into conventional processing chain for different BCI applications.

DEVELOPMENT OF A REAL-TIME SINGLE CHANNEL BRAIN–COMPUTER INTERFACE SYSTEM FOR DETECTION OF DROWSINESS

Drowsiness or fatigue condition refers to feeling abnormally sleepy at an inappropriate time, especially during day time. It reduces the level of concentration and slowdown the response time, which eventually increases the error rate while doing any day-to-day activity. It can be dangerous for some people who require higher concentration level while doing their work. Study shows that 25–30% of road accidents occur due to drowsy driving. There are number of methods available for the detection of drowsiness out of which most of the methods provide an indirect measurement of drowsiness whereas electroencephalography provides the most reliable and direct measurement of the level of consciousness of the subject. The aim of this paper is to design and develop a portable and low cost brain–computer interface system for detection of drowsiness. In this study, we are using three dry electrodes out of which two active electrodes are placed on the forehead whereas the reference electrode is placed on the earlobe to acquire electroencephalogram (EEG) signal. Previous research shows that, there is a measurable change in the amplitude of theta ([Formula: see text]) wave and alpha ([Formula: see text]) wave between the active state and the drowsy state and based on this fact theta ([Formula: see text]) wave and alpha ([Formula: see text]) wave are separated from the normal EEG signal. The signal processing unit is interfaced with the microcontroller unit which is programmed to analyze the drowsiness based on the change in the amplitude of theta ([Formula: see text]) wave. An alarm will be activated once drowsiness is detected. The experiment was conducted on 20 subjects and EEG data were recorded to develop our drowsiness detection system. Experimental results have proved that our system has achieved real-time drowsiness detection with an accuracy of approximately 85%.

Asynchronous Detection of Trials Onset from Raw EEG Signals

Clinical processing of event-related potentials (ERPs) requires a precise synchrony between the stimulation and the acquisition units that are guaranteed by means of a physical link between them. This precise synchrony is needed since temporal misalignments during trial averaging can lead to high deviations of peak times, thus causing error in diagnosis or inefficiency in classification in brain-computer interfaces (BCIs). Out of the laboratory, mobile EEG systems and BCI headsets are not provided with the physical link, thus being inadequate for acquisition of ERPs. In this study, we propose a method for the asynchronous detection of trials onset from raw EEG without physical links. We validate it with a BCI application based on the dichotic listening task. The user goal was to attend the cued auditory message and to report three keywords contained in it while ignoring the other message. The BCI goal was to detect the attended message from the analysis of auditory ERPs. The rate of successful onset detection in both synchronous (using the real onset) and asynchronous (blind detection of trial onset from raw EEG) was 73% with a synchronization error of less than 1[Formula: see text]ms. The level of synchronization provided by this proposal would allow home-based acquisition of ERPs with low cost BCI headsets and any media player unit without physical links between them.

Classification of Electroencephalogram Data from Hand Grasp and Release Movements for BCI Controlled Prosthesis

The use of body-powered prostheses can be tiring and lead to further problems with compliance and prosthetic restoration. Brain-Computer-Interfaces (BCI) offers a mean of controlling prostheses for patients that otherwise are unable to operate such devices due to physical limitations. An issue with BCIs is that they tend to either require invasive recording methods, posing a surgical risk, or work by generating control signals from not task related brain activity patterns such as right vs. left hand, hand vs. leg or visual stimulation and therefore are not intuitive in their control. This research aims to test the possibility of controlling the grasp and release of an upper limb prosthetic terminal device by classifying Electroencephalogram (EEG) data from real hand grasping and releasing movement. Data from five healthy subjects were recorded using a consumer grade non-invasive Emotiv EPOC headset. During the measurement the subjects were asked to perform isometric finger extension and flexion of their right hand. In order to bring the EEG data into correlation with the executed movement a simultaneous electromyogram (EMG) recording is proposed as an alternative method to recordings of visual cued movement. Classified EMG data was used to generate markers in the EEG data and to epoch the data. In order to increase the signal to noise ratio and allow better classification, the EEG data was filtered and spectrally weighted common spatial patterns (spec-CSP) were used for feature extraction. Using linear discriminant analysis a classification rate of up to 73.2% between grasp and release was achieved. In this work, a novel EMG-assisted approach has been developed for classification of EEG data from hand grasp and release movements. It shows feasibility for a more intuitive control of upper limb prosthetic terminal device using low-cost BCI without the risk associated with invasive measurement.