Non-invasive Brain-computer Interface System Research Articles

Two-stage sparse multi-objective evolutionary algorithm for channel selection optimization in BCIs.

Channel selection has become the pivotal issue affecting the widespread application of non-invasive brain-computer interface systems in the real world. However, constructing suitable multi-objective problem models alongside effective search strategies stands out as a critical factor that impacts the performance of multi-objective channel selection algorithms. This paper presents a two-stage sparse multi-objective evolutionary algorithm (TS-MOEA) to address channel selection problems in brain-computer interface systems. In TS-MOEA, a two-stage framework, which consists of the early and late stages, is adopted to prevent the algorithm from stagnating. Furthermore, The two stages concentrate on different multi-objective problem models, thereby balancing convergence and population diversity in TS-MOEA. Inspired by the sparsity of the correlation matrix of channels, a sparse initialization operator, which uses a domain-knowledge-based score assignment strategy for decision variables, is introduced to generate the initial population. Moreover, a Score-based mutation operator is utilized to enhance the search efficiency of TS-MOEA. The performance of TS-MOEA and five other state-of-the-art multi-objective algorithms has been evaluated using a 62-channel EEG-based brain-computer interface system for fatigue detection tasks, and the results demonstrated the effectiveness of TS-MOEA. The proposed two-stage framework can help TS-MOEA escape stagnation and facilitate a balance between diversity and convergence. Integrating the sparsity of the correlation matrix of channels and the problem-domain knowledge can effectively reduce the computational complexity of TS-MOEA while enhancing its optimization efficiency.

Domain knowledge-assisted multi-objective evolutionary algorithm for channel selection in brain-computer interface systems.

For non-invasive brain-computer interface systems (BCIs) with multiple electroencephalogram (EEG) channels, the key factor limiting their convenient application in the real world is how to perform reasonable channel selection while ensuring task accuracy, which can be modeled as a multi-objective optimization problem. Therefore, this paper proposed a two-objective problem model for the channel selection problem and introduced a domain knowledge-assisted multi-objective optimization algorithm (DK-MOEA) to solve the aforementioned problem. The multi-objective optimization problem model was designed based on the channel connectivity matrix and comprises two objectives: one is the task accuracy and the other one can sensitively indicate the removal status of channels in BCIs. The proposed DK-MOEA adopted a two-space framework, consisting of the population space and the knowledge space. Furthermore, a knowledge-assisted update operator was introduced to enhance the search efficiency of the population space by leveraging the domain knowledge stored in the knowledge space. The proposed two-objective problem model and DK-MOEA were tested on a fatigue detection task and four state-of-the-art multi-objective evolutionary algorithms were used for comparison. The experimental results indicated that the proposed algorithm achieved the best results among all the comparative algorithms for most cases by the Wilcoxon rank sum test at a significance level of 0.05. DK-MOEA was also compared with a version without the utilization of domain knowledge and the experimental results validated the effectiveness of the knowledge-assisted mutation operator. Moreover, the comparison between DK-MOEA and a traditional classification algorithm using all channels demonstrated that DK-MOEA can strike the balance between task accuracy and the number of selected channels. The formulated two-objective optimization model enabled the selection of a minimal number of channels without compromising classification accuracy. The utilization of domain knowledge improved the performance of DK-MOEA. By adopting the proposed two-objective problem model and DK-MOEA, a balance can be achieved between the number of the selected channels and the accuracy of the fatigue detection task. The methods proposed in this paper can reduce the complexity of subsequent data processing and enhance the convenience of practical applications.

Comprehensive review on brain-controlled mobile robots and robotic arms based on electroencephalography signals

There is a significant progress in the development of brain-controlled mobile robots and robotic arms in the recent years. New advances in electroencephalography (EEG) technology have led to the possibility of controlling external devices, such as robots, directly via the brain. The development of brain-controlled robotic devices has allowed people with bodily disabilities to enhance their mobility, individuality, and many types of activity. This paper provides a comprehensive review of EEG signal processing in robot control, including mobile robots and robotic arms, especially based on noninvasive brain computer interface systems. Various filtering approaches, feature extraction techniques, and machine learning algorithms for EEG classification are discussed and summarized. Finally, the conditions of the environments in which robots are used and robot types are also discussed.

A Novel P300 Classification Algorithm Based on a Principal Component Analysis-Convolutional Neural Network

Aiming at enhancing the classification accuracy of P300 Electroencephalogram signals in a non-invasive brain–computer interface system, a novel P300 electroencephalogram signals classification algorithm is proposed which is based on improved convolutional neural network. In the data preprocessing part, the proposed P300 classification algorithm used the Principal Component Analysis algorithm to not only remove the noise and artifacts in the data, but also increase the data processing speed. Furthermore, the proposed P300 classification algorithm employed the parallel convolution method to improve the traditional convolutional neural network framework, which can increase the network depth and improve the network’s ability to classify P300 electroencephalogram signals. The proposed algorithm was evaluated by two datasets (the dataset from the competition and the dataset from the laboratory). The results show that, in the dataset I, the proposed P300 classification algorithm could obtain accuracy rates higher than 95%, and achieve one of the best performances in four classification algorithms, while, in the dataset II, the proposed P300 classification algorithm can get accuracy rates higher than 90%, and is superior to the other three algorithms in all ten subjects. These demonstrated the effectiveness of the proposed algorithm. The proposed classification algorithm can be applied in the actual brain–computer interface systems to help people with disability in the daily lives.

Optimising non-invasive brain-computer interface systems for free communication between na\xefve human participants

Free communication is one of the cornerstones of modern civilisation. While manual keyboards currently allow us to interface with computers and manifest our thoughts, a next frontier is communication without manual input. Brain-computer interface (BCI) spellers often achieve this by decoding patterns of neural activity as users attend to flickering keyboard displays. To date, the highest performing spellers report typing rates of ~10.00 words/minute. While impressive, these rates are typically calculated for experienced users repetitively typing single phrases. It is therefore not clear whether naïve users are able to achieve such high rates with the added cognitive load of genuine free communication, which involves continuously generating and spelling novel words and phrases. In two experiments, we developed an open-source, high-performance, non-invasive BCI speller and examined its feasibility for free communication. The BCI speller required users to focus their visual attention on a flickering keyboard display, thereby producing unique cortical activity patterns for each key, which were decoded using filter-bank canonical correlation analysis. In Experiment 1, we tested whether seventeen naïve users could maintain rapid typing during prompted free word association. We found that information transfer rates were indeed slower during this free communication task than during typing of a cued character sequence. In Experiment 2, we further evaluated the speller’s efficacy for free communication by developing a messaging interface, allowing users to engage in free conversation. The results showed that free communication was possible, but that information transfer was reduced by voluntary textual corrections and turn-taking during conversation. We evaluated a number of factors affecting the suitability of BCI spellers for free communication, and make specific recommendations for improving classification accuracy and usability. Overall, we found that developing a BCI speller for free communication requires a focus on usability over reduced character selection time, and as such, future performance appraisals should be based on genuine free communication scenarios.

Measuring and monitoring emotional changes in children who stutter

The assessment of clients with speech disorders presents challenges for speech-language pathologists. For example, having a reliable way of measuring the severity of the case, determining which remedial program is aligned with a patient's needs, and measuring of treatment processes. There is potential for brain-computer interface (BCI) applications to enhance speech therapy sessions by providing objective insights and real-time visualization of brain activity during the sessions. This paper presents a study on emotional state detection during speech pathology. The goal of this study is to investigate affective–motivational brain responses to stimuli in children who stutter. To this end, we conducted an experiment that involved recording frontal electroencephalography (EEG) activity from fifteen children with stuttering whilst they looked at visual stimuli. The contribution of our study is to provide a comprehensive background and a framework for emotional state detection experiments as assessment and monitoring tool in speech pathology. It mainly discusses the feasibility and potential benefits of applying EEG-based emotion detection in speech-language therapy contexts of use. The findings of our research indicate that emotional recognition using non-invasive EEG-based BCI system is sufficient to differentiate between affective states of individuals in treatment contexts.

Design of Embedded System for Multivariate Classification of Finger and Thumb Movements Using EEG Signals for Control of Upper Limb Prosthesis.

Brain Computer Interface (BCI) determines the intent of the user from a variety of electrophysiological signals. These signals, Slow Cortical Potentials, are recorded from scalp, and cortical neuronal activity is recorded by implanted electrodes. This paper is focused on design of an embedded system that is used to control the finger movements of an upper limb prosthesis using Electroencephalogram (EEG) signals. This is a follow-up of our previous research which explored the best method to classify three movements of fingers (thumb movement, index finger movement, and first movement). Two-stage logistic regression classifier exhibited the highest classification accuracy while Power Spectral Density (PSD) was used as a feature of the filtered signal. The EEG signal data set was recorded using a 14-channel electrode headset (a noninvasive BCI system) from right-handed, neurologically intact volunteers. Mu (commonly known as alpha waves) and Beta Rhythms (8–30 Hz) containing most of the movement data were retained through filtering using “Arduino Uno” microcontroller followed by 2-stage logistic regression to obtain a mean classification accuracy of 70%.

Comparing Common Average Referencing to Laplacian Referencing in Detecting Imagination and Intention of Movement for Brain Computer Interface

Brain-computer interface (BCI) is a paradigm that offers an alternative communication channel between neural activity generated in the brain and the user’s external environment. This paper investigates detection of intention of movement from surface EEG during actual and imagination of movement which is essential for developing non-invasive BCI system for neuro-impaired patients. EEG signal was recorded from 11 subjects while imagining and performing right wrist movement in multiple directions using 28 electrodes based on international 10-20 standard electrode placement locations. The recorded EEG signal later was filtered and pre-processed by spatial filter namely; Common average reference (CAR) and Laplacian (LAP) filter. Features were extracted from the filtered signal using ERSP and power spectrum and classified by k-nearest neighbour (k-NN) and quadratic discriminant analysis (QDA) classifiers. The classification results show that LAP filter has outperformed CAR with respect to classification. Classification accuracy ranged from 63.33% to 100% for detection of imagination of movement and 60% to 96.67% for detection of intention of actual movement. In both of detection of imagination and intention of movement k-NN classifier gave better result compared to QDA classifier.

Usability of non-invasive brain computer interface systems for upper limb recovery after stroke

Usability of non-invasive brain computer interface systems for upper limb recovery after stroke

Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain–computer interface

Objective. At the balanced intersection of human and machine adaptation is found the optimally functioning brain–computer interface (BCI). In this study, we report a novel experiment of BCI controlling a robotic quadcopter in three-dimensional (3D) physical space using noninvasive scalp electroencephalogram (EEG) in human subjects. We then quantify the performance of this system using metrics suitable for asynchronous BCI. Lastly, we examine the impact that the operation of a real world device has on subjects' control in comparison to a 2D virtual cursor task. Approach. Five human subjects were trained to modulate their sensorimotor rhythms to control an AR Drone navigating a 3D physical space. Visual feedback was provided via a forward facing camera on the hull of the drone. Main results. Individual subjects were able to accurately acquire up to 90.5% of all valid targets presented while travelling at an average straight-line speed of 0.69 m s−1. Significance. Freely exploring and interacting with the world around us is a crucial element of autonomy that is lost in the context of neurodegenerative disease. Brain–computer interfaces are systems that aim to restore or enhance a user's ability to interact with the environment via a computer and through the use of only thought. We demonstrate for the first time the ability to control a flying robot in 3D physical space using noninvasive scalp recorded EEG in humans. Our work indicates the potential of noninvasive EEG-based BCI systems for accomplish complex control in 3D physical space. The present study may serve as a framework for the investigation of multidimensional noninvasive BCI control in a physical environment using telepresence robotics.

2A2-P18 BCIに用いる頭皮上脳波の検討(非接触センシング)

Objective of this study is development of a BCI (Brain Computer interface) system using noninvasive EEG (Electroencephalogram) that is measured from the scalp. In this paper, We described about an our developed EEG measurement system, measured EEG of holding right hand by using this system and analyzed in order to find the features. From results of experiments, I think that our system will be useful for the study of developing noninvasive BCI system.

1A2-E04 頭皮上脳波を用いたBCIシステムの開発(非接触センシング)

Objective of this study is development of a BCI (Brain Computer interface) system using noninvasive EEG (Electroencephalogram) that is measured from the scalp. As background research, the number of disabled persons in the country exceeded 3.5 million at the time of 2008. the number mentioned that increasing tendency. Especially, impaired limbs close to 50 percent and about 1.7 million people. In this paper, We described about an our developed EEG measurement system, measured EEG of holding right hand by using this system and analyzed in order to find the features. From results of experiments, I think that our system will be useful for the study of developing noninvasive BCI system.

Fast attainment of computer cursor control with noninvasively acquired brain signals

Brain–computer interface (BCI) systems are allowing humans and non-human primates to drive prosthetic devices such as computer cursors and artificial arms with just their thoughts. Invasive BCI systems acquire neural signals with intracranial or subdural electrodes, while noninvasive BCI systems typically acquire neural signals with scalp electroencephalography (EEG). Some drawbacks of invasive BCI systems are the inherent risks of surgery and gradual degradation of signal integrity. A limitation of noninvasive BCI systems for two-dimensional control of a cursor, in particular those based on sensorimotor rhythms, is the lengthy training time required by users to achieve satisfactory performance. Here we describe a novel approach to continuously decoding imagined movements from EEG signals in a BCI experiment with reduced training time. We demonstrate that, using our noninvasive BCI system and observational learning, subjects were able to accomplish two-dimensional control of a cursor with performance levels comparable to those of invasive BCI systems. Compared to other studies of noninvasive BCI systems, training time was substantially reduced, requiring only a single session of decoder calibration (∼20 min) and subject practice (∼20 min). In addition, we used standardized low-resolution brain electromagnetic tomography to reveal that the neural sources that encoded observed cursor movement may implicate a human mirror neuron system. These findings offer the potential to continuously control complex devices such as robotic arms with one's mind without lengthy training or surgery.

Brain-Computer Interface Controlled Functional Electrical Stimulation System for Ankle Movement

BackgroundMany neurological conditions, such as stroke, spinal cord injury, and traumatic brain injury, can cause chronic gait function impairment due to foot-drop. Current physiotherapy techniques provide only a limited degree of motor function recovery in these individuals, and therefore novel therapies are needed. Brain-computer interface (BCI) is a relatively novel technology with a potential to restore, substitute, or augment lost motor behaviors in patients with neurological injuries. Here, we describe the first successful integration of a noninvasive electroencephalogram (EEG)-based BCI with a noninvasive functional electrical stimulation (FES) system that enables the direct brain control of foot dorsiflexion in able-bodied individuals.MethodsA noninvasive EEG-based BCI system was integrated with a noninvasive FES system for foot dorsiflexion. Subjects underwent computer-cued epochs of repetitive foot dorsiflexion and idling while their EEG signals were recorded and stored for offline analysis. The analysis generated a prediction model that allowed EEG data to be analyzed and classified in real time during online BCI operation. The real-time online performance of the integrated BCI-FES system was tested in a group of five able-bodied subjects who used repetitive foot dorsiflexion to elicit BCI-FES mediated dorsiflexion of the contralateral foot.ResultsFive able-bodied subjects performed 10 alternations of idling and repetitive foot dorsifiexion to trigger BCI-FES mediated dorsifiexion of the contralateral foot. The epochs of BCI-FES mediated foot dorsifiexion were highly correlated with the epochs of voluntary foot dorsifiexion (correlation coefficient ranged between 0.59 and 0.77) with latencies ranging from 1.4 sec to 3.1 sec. In addition, all subjects achieved a 100% BCI-FES response (no omissions), and one subject had a single false alarm.ConclusionsThis study suggests that the integration of a noninvasive BCI with a lower-extremity FES system is feasible. With additional modifications, the proposed BCI-FES system may offer a novel and effective therapy in the neuro-rehabilitation of individuals with lower extremity paralysis due to neurological injuries.

Non-invasive brain–computer interface system: Towards its application as assistive technology

The quality of life of people suffering from severe motor disabilities can benefit from the use of current assistive technology capable of ameliorating communication, house-environment management and mobility, according to the user's residual motor abilities. Brain–computer interfaces (BCIs) are systems that can translate brain activity into signals that control external devices. Thus they can represent the only technology for severely paralyzed patients to increase or maintain their communication and control options. Here we report on a pilot study in which a system was implemented and validated to allow disabled persons to improve or recover their mobility (directly or by emulation) and communication within the surrounding environment. The system is based on a software controller that offers to the user a communication interface that is matched with the individual's residual motor abilities. Patients ( n = 14) with severe motor disabilities due to progressive neurodegenerative disorders were trained to use the system prototype under a rehabilitation program carried out in a house-like furnished space. All users utilized regular assistive control options (e.g., microswitches or head trackers). In addition, four subjects learned to operate the system by means of a non-invasive EEG-based BCI. This system was controlled by the subjects’ voluntary modulations of EEG sensorimotor rhythms recorded on the scalp; this skill was learnt even though the subjects have not had control over their limbs for a long time. We conclude that such a prototype system, which integrates several different assistive technologies including a BCI system, can potentially facilitate the translation from pre-clinical demonstrations to a clinical useful BCI.