Single-Channel Region-Based Speller for Controlling Home Appliances

The brain-computer interface (BCI) system uses electroencephalography (EEG) signals for correspondence between the human and the outside world. This BCI communication system does not require any muscle action; hence, it can be controlled with the help of brain activities only. Therefore, this kind of system is helpful for patients, who are completely paralyzed or suffering from diseases like ALS (Amyotrophic Lateral Sclerosis), and spinal cord injury, etc., but having a normal functioning brain. A region-based P300 speller system for controlling home electronic appliances is proposed in this article. With the help of the proposed system, users can control and use appliances like an electronic door, fan, light, system, etc., without carrying out any physical movement. The experiments are conducted for five, ten, and fifteen trails for each subject. Among all classifiers, the ANN classifier provides the best off-line experiment accuracy of the order of 80% for fifteen flashes. Moreover, for the control translation, the Arduino module is also designed which is low cost and low power-based and physically controlled a device.

An FPGA-Embedded Brain-Computer Interface System to Support Individual Autonomy in Locked-In Individuals

Brain-computer interfaces (BCI) can detect specific EEG patterns and translate them into control signals for external devices by providing people suffering from severe motor disabilities with an alternative/additional channel to communicate and interact with the outer world. Many EEG-based BCIs rely on the P300 event-related potentials, mainly because they require training times for the user relatively short and provide higher selection speed. This paper proposes a P300-based portable embedded BCI system realized through an embedded hardware platform based on FPGA (field-programmable gate array), ensuring flexibility, reliability, and high-performance features. The system acquires EEG data during user visual stimulation and processes them in a real-time way to correctly detect and recognize the EEG features. The BCI system is designed to allow to user to perform communication and domotic controls.

Optimizing a left and right visual field biphasic stimulation paradigm for SSVEP-based BCIs with hairless region behind the ear

Objective. Steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) has the characteristics of fast communication speed, high stability, and wide applicability, thus it has been widely studied. With the rapid development in paradigm, algorithm, and system design, SSVEP-BCI is gradually applied in clinical and real-life scenarios. In order to improve the ease of use of the SSVEP-BCI system, many studies have been focusing on developing it on the hairless area, but due to the lack of redesigning the stimulation paradigm to better adapt to the new area, the EEG response in the hairless area is worse than occipital region. Approach. This study first proposed a phase difference estimation method based on stimulating the left and right visual field separately, then developed and optimized a left and right visual field biphasic stimulation paradigm for SSVEP-based BCIs with hairless region behind the ear. Main results. In the 12-target online experiment, after a short model estimation training, all sixteen subjects used their best stimulus condition. The paradigm designed in this study can increase the proportion of applicable subjects for the behind-ear SSVEP-BCI system from 58.3% to 75% and increase the accuracy from 74.6±20.0% (the existing best SSVEP stimulus with hairless region behind the ear) to 84.2±14.7%, and the ITR from 14.2±6.4bits/min to 17.8±5.7bits/min. Significance. These results demonstrated that the proposed paradigm can effectively improve the BCI performance using the signal from the hairless region behind the ear, compared with the standard SSVEP stimulation paradigm.

Brain–computer interfaces for human gait restoration

In this review article, we present more than a decade of our work on the development of brain–computer interface (BCI) systems for the restoration of walking following neurological injuries such as spinal cord injury (SCI) or stroke. Most of this work has been in the domain of non-invasive electroencephalogram-based BCIs, including interfacing our system with a virtual reality environment and physical prostheses. Real-time online tests are presented to demonstrate the ability of able-bodied subjects as well as those with SCI to purposefully operate our BCI system. Extensions of this work are also presented and include the development of a portable low-cost BCI suitable for at-home use, our ongoing efforts to develop a fully implantable BCI for the restoration of walking and leg sensation after SCI, and our novel BCI-based therapy for stroke rehabilitation.

Wheelchair controlled by human brainwave using brain-computer interface system for paralyzed patient

Integrated wheelchair controlled by human brainwave using a brain-computer interface (BCI) system was designed to help disabled people. The invention aims to improve the development of integrated wheelchair using a BCI system, depending on the ability individual brain attention level. An electroencephalography (EEG) device called mindwave mobile plus (MW+) has been employed to obtain the attention value for wheelchair movement, eye blink to change the mode of the wheelchair to move forward (F), to the right (R), backward (B) and to the left (L). Stop mode (S) is selected when doing eyebrow movement as the signal quality value of 26 or 51 is produced. The development of the wheelchair controlled by human brainwave using a BCI system for helping a paralyzed patient shows the efficiency of the brainwave integrated wheelchair and improved using human attention value, eye blink detection and eyebrow movement. Also, analysis of the human attention value in different gender and age category also have been done to improve the accuracy of the brainwave integrated wheelchair. The threshold value for male children is 60, male teenager (70), male adult (40) while for female children is 50, female teenager (50) and female adult (30).

Hardware Design of a Tele-EEG Device For Detection of Neurophysiological Condition

The main idea of this research originates from the patients such that each patient with neural disorder should refer to a medical center to check his brain's health condition to sample his EEG signals and present the results to a specialist for further investigation. If this process can be done remotely by tele-medicine techniques, it will save cost and time. In tele-medicine method, the patient can record the EEG signal alone at home and send the results to his physician. To this end, this research employs Bluetooth to connect the interface system to the computer, and the patient can send the results to his physician after saving the data. Thus, the main purpose of designing this BCI system is to record EEG signals using a microcontroller and transmit them via Bluetooth to a computer and mobile phone such that the signal can be represented instantaneously in a GUI.

Electroencephalogram-Based Motor Imagery Classification Using Deep Residual Convolutional Networks

The classification of electroencephalogram (EEG) signals is of significant importance in brain-computer interface (BCI) systems. Aiming to achieve intelligent classification of motor imagery EEG types with high accuracy, a classification methodology using the wavelet packet decomposition (WPD) and the proposed deep residual convolutional networks (DRes-CNN) is proposed. Firstly, EEG waveforms are segmented into sub-signals. Then the EEG signal features are obtained through the WPD algorithm, and some selected wavelet coefficients are retained and reconstructed into EEG signals in their respective frequency bands. Subsequently, the reconstructed EEG signals were utilized as input of the proposed deep residual convolutional networks to classify EEG signals. Finally, EEG types of motor imagination are classified by the DRes-CNN classifier intelligently. The datasets from BCI Competition were used to test the performance of the proposed deep learning classifier. Classification experiments show that the average recognition accuracy of this method reaches 98.76%. The proposed method can be further applied to the BCI system of motor imagination control.

Residential buildings with brain-computer interface functionality: An elevator case study

Brain–computer interfaces (BCI) are systems that use signals recorded from the brain to enable communication and control applications. One of the most important applications of BCI technology is that enables people who are severely paralyzed by amyotrophic lateral sclerosis, brainstem stroke, or other disorders to communicate, operate computer programs, or even control numerous devices. Moreover, elevators are probably the best option for disabled persons to expand their access and mobility within a house or a building. In this study, a prototype application is presented, together with an experimental setup of a BCI system that attempts to control an elevator. Practical application Many researchers are dealing with BCI systems that give the possibility to disabled people to control a variety of devices from wheelchairs to different home appliances, using the signals of their brain and forming a smart home services framework. This work comes to support this effort by presenting a case study, as a proof of concept, for an elevator BCI system that could be part of a complete “smart” home BCI system. The presented experimental setup proves that elevators with BCI functionalities are practically feasible and in an affordable cost, and that they could be a significant element within a “smart” residential building.

Hardware Design of a Tele-EEG Device For Detection of Neurophysiological Condition

The main idea of this research originates from the patients such that each patient with neural disorder should refer to a medical center to check his brain's health condition to sample his EEG signals and present the results to a specialist for further investigation. If this process can be done remotely by tele-medicine techniques, it will save cost and time. In tele-medicine method, the patient can record the EEG signal alone at home and send the results to his physician. To this end, this research employs Bluetooth to connect the interface system to the computer, and the patient can send the results to his physician after saving the data. Thus, the main purpose of designing this BCI system is to record EEG signals using a microcontroller and transmit them via Bluetooth to a computer and mobile phone such that the signal can be represented instantaneously in a GUI.