Brain-computer Interface Control System Research Articles

Design of Brain Computer Interface Control System Based on Neural Network

Design of Brain Computer Interface Control System Based on Neural Network

A STUDY ON NEURO FUZZY ALGORITHM IMPLEMENTATION ON BCI-UAV CONTROL SYSTEMS

Brain-Computer Interface (BCI) machines are capable of obtaining brain activities by conducting Electroencephalogram tests. Developments on both BCI and Machine Learning allowed various researchers to develop and study various BCI control systems, mainly varying with the algorithm implementation. This research presents a performance analysis of the Adaptive Neuro-Fuzzy Inference System (ANFIS) for BCI control systems for drone maneuverability. Eye gestures were used to generate the EEG data that were captured using the Emotiv INSIGHT Neuroheadset. The obtained data were transferred to the computing hardware using IEEE 802.15 wirless communication protocol (i.e. Bluetooth connectivity); the data are processed using the 5th order Butterworth Band-Pass filtering and heuristic filtering. The filtered dataset is then fed to the ANFIS and a Support Vector Machine (SVM) algorithm, the latter serving as the basis, for training and quadcopter control implementation. Three flight tests were done, hover test, flight command test, and the flight control test, the final test compared the performance of the BCI control system using the ANFIS algorithm to the performance of a traditional handheld remote controller. Results from the initial two tests showed that the ANFIS performed comparably with the SVM, and even about 2% better. The final test showed that the BCI control system had a maximum variance of 4% compared to the handheld controller, where the latter served as the basis. It was found that between Machine Learning algorithms, ANFIS is as capable as the SVM for BCI control systems. Further developments may focus on employing time-series EEG preprocessing techniques.

Toward a Brain-Computer Interface- and Internet of Things-Based Smart Ward Collaborative System Using Hybrid Signals.

This study proposes a brain-computer interface (BCI)- and Internet of Things (IoT)-based smart ward collaborative system using hybrid signals. The system is divided into hybrid asynchronous electroencephalography (EEG)-, electrooculography (EOG)- and gyro-based BCI control system and an IoT monitoring and management system. The hybrid BCI control system proposes a GUI paradigm with cursor movement. The user uses the gyro to control the cursor area selection and uses blink-related EOG to control the cursor click. Meanwhile, the attention-related EEG signals are classified based on a support-vector machine (SVM) to make the final judgment. The judgment of the cursor area and the judgment of the attention state are reduced, thereby reducing the false operation rate in the hybrid BCI system. The accuracy in the hybrid BCI control system was 96.65 ± 1.44%, and the false operation rate and command response time were 0.89 ± 0.42 events/min and 2.65 ± 0.48 s, respectively. These results show the application potential of the hybrid BCI control system in daily tasks. In addition, we develop an architecture to connect intelligent things in a smart ward based on narrowband Internet of Things (NB-IoT) technology. The results demonstrate that our system provides superior communication transmission quality.

Motor imagery-based EEG signals classification by combining temporal and spatial deep characteristics

PurposeIn order to improve the weak recognition accuracy and robustness of the classification algorithm for brain-computer interface (BCI), this paper proposed a novel classification algorithm for motor imagery based on temporal and spatial characteristics extracted by using convolutional neural networks (TS-CNN) model.Design/methodology/approachAccording to the proposed algorithm, a five-layer neural network model was constructed to classify the electroencephalogram (EEG) signals. Firstly, the author designed a motor imagery-based BCI experiment, and four subjects were recruited to participate in the experiment for the recording of EEG signals. Then, after the EEG signals were preprocessed, the temporal and spatial characteristics of EEG signals were extracted by longitudinal convolutional kernel and transverse convolutional kernels, respectively. Finally, the classification of motor imagery was completed by using two fully connected layers.FindingsTo validate the classification performance and efficiency of the proposed algorithm, the comparative experiments with the state-of-the-arts algorithms are applied to validate the proposed algorithm. Experimental results have shown that the proposed TS-CNN model has the best performance and efficiency in the classification of motor imagery, reflecting on the introduced accuracy, precision, recall, ROC curve and F-score indexes.Originality/valueThe proposed TS-CNN model accurately recognized the EEG signals for different tasks of motor imagery, and provided theoretical basis and technical support for the application of BCI control system in the field of rehabilitation exoskeleton.

Conceptual Design of BCI for Mobile Robot Control

This paper presents an application of Hierarchical Systems (HS) technology in conceptual and detailed design of Brain Computer Interface (BCI) system to control a mobile robot. The BCI is a biomechatronic system that includes biological (brain), computer (control PC), electronic (sensors), visual informatics (LCD—liquid crystal display, GUI—graphic user interface) and executive electro-mechanical (MR—mobile robot) subsystems. Therefore, the conceptual model of the designed BCI system should present connected formal models of the above subsystems presented in the general systemic basis. The structure of the BCI system, its dynamic realization as a unit in its environment and MR environment are presented formally as well. In addition, the conceptual model should also take into account the BCI inter-level relations performed by MR coordinator implemented in the form of the design and control system. Therefore, HS model (and its standard block aed—ancient Greek word) is selected and described as the formal basis of the conceptual model of BCI system in the first part of the given paper. BCI system detailed design is under consideration in the second part of the paper. BCI control system and MR design results, as well as MR control process are also described in the final part of the paper.

Quadcopter Control System Using a Hybrid BCI Based on Off-Line Optimization and Enhanced Human-Machine Interaction

Quadcopter is an important way for the human to explore the physical world. The brain-computer interface (BCI) technology is used to control the quadcopter flight in order to help disabled persons communicate with the external world freely. In this study, a quadcopter control system using a hybrid BCI based on off-line optimization and enhanced human-machine interaction was designed to control the quadcopter flight in 3D physical space. The proposed system implemented the control of quadcopter moving up/down, forward/backward, left/right by six different SSVEP, and turning left/right by left-hand and right-hand motor imagery. Meanwhile, the optimization of the control system and the human-machine interaction enhancement improved practicability in real-time use. Five subjects participated in an on-line experiment to control the quadcopter flight in real-time. The average classification accuracy of EEG-based commands in the on-line experiment was 87.09±2.82% and information transfer rate (ITR) was 0.857±0.085 bits/min. The results demonstrated the feasibility of multidirectional control of quadcopter flight in 3D space by using hybrid BCI technology and revealed the practicality and operability of the hybrid BCI control system based on off-line optimization and human-machine interaction enhancement.

The Use of a Brain Computer Interface Remote Control to Navigate a Recreational Device

People suffering from paralysis caused by serious neural disorder or spinal cord injury also need to be given a means of recreation other than general living aids. Although there have been a proliferation of brain computer interface (BCI) applications, developments for recreational activities are scarcely seen. The objective of this study is to develop a BCI-based remote control integrated with commercial devices such as the remote controlled Air Swimmer. The brain is visually stimulated using boxes flickering at preprogrammed frequencies to activate a brain response. After acquiring and processing these brain signals, the frequency of the resulting peak, which corresponds to the user’s selection, is determined by a decision model. Consequently, a command signal is sent from the computer to the wireless remote controller via a data acquisition (DAQ) module. A command selection training (CST) and simulated path test (SPT) were conducted by 12 subjects using the BCI control system and the experimental results showed a recognition accuracy rate of 89.51% and 92.31% for the CST and SPT, respectively. The fastest information transfer rate demonstrated a response of 105 bits/min and 41.79 bits/min for the CST and SPT, respectively. The BCI system was proven to be able to provide a fast and accurate response for a remote controller application.