Towards Real-World BCI: CCSPNet, A Compact Subject-Independent Motor Imagery Framework

A conventional subject-dependent (SD) brain-computer interface (BCI) requires a complete data-gathering, training, and calibration phase for each user before it can be used. In recent years, a number of subject-independent (SI) BCIs have been developed. However, there are many problems preventing them from being used in real-world BCI applications. A weaker performance compared to the subject-dependent (SD) approach, and a relatively large model requiring high computational power are the most important ones. Therefore, a potential real-world BCI would greatly benefit from a compact low-power subject-independent BCI framework, ready to be used immediately after the user puts it on. To move towards this goal, we propose a novel subject-independent BCI framework named CCSPNet (Convolutional Common Spatial Pattern Network) trained on the motor imagery (MI) paradigm of a large-scale electroencephalography (EEG) signals database consisting of 21600 trials for 54 subjects performing two-class hand-movement MI tasks. The proposed framework applies a wavelet kernel convolutional neural network (WKCNN) and a temporal convolutional neural network (TCNN) in order to represent and extract the diverse spectral features of EEG signals. The outputs of the convolutional layers go through a common spatial pattern (CSP) algorithm for spatial feature extraction. The number of CSP features is reduced by a dense neural network, and the final class label is determined by a linear discriminative analysis (LDA) classifier. The CCSPNet framework evaluation results show that it is possible to have a low-power compact BCI that achieves both SD and SI performance comparable to complex and computationally expensive.Comment: 15 pages, 6 figures, 6 tables, 1 algorith

Quadcopter Flight Control Using a Non-invasive Multi-Modal Brain Computer Interface

Brain-Computer Interfaces (BCIs) translate neuronal information into commands to control external software or hardware, which can improve the quality of life for both healthy and disabled individuals. Here, a multi-modal BCI which combines motor imagery (MI) and steady-state visual evoked potential (SSVEP) is proposed to achieve stable control of a quadcopter in three-dimensional physical space. The complete information common spatial pattern (CICSP) method is used to extract two MI features to control the quadcopter to fly left-forward and right-forward, and canonical correlation analysis (CCA) is employed to perform the SSVEP classification for rise and fall. Eye blinking is designed to switch these two modes while hovering. Real-time feedback is provided to subjects by a global camera. Two flight tasks were conducted in physical space in order to certify the reliability of the BCI system. Subjects were asked to control the quadcopter to fly forward along the zig-zag pattern to pass through a gate in the relatively simple task. For the other complex task, the quadcopter was controlled to pass through two gates successively according to an S-shaped route. The performance of the BCI system is quantified using suitable metrics and subjects are able to acquire 86.5% accuracy for the complicated flight task. It is demonstrated that the multi-modal BCI has the ability to increase the accuracy rate, reduce the task burden, and improve the performance of the BCI system in the real world

A comprehensive review on motion trajectory reconstruction for EEG-based brain-computer interface

The advance in neuroscience and computer technology over the past decades have made brain-computer interface (BCI) a most promising area of neurorehabilitation and neurophysiology research. Limb motion decoding has gradually become a hot topic in the field of BCI. Decoding neural activity related to limb movement trajectory is considered to be of great help to the development of assistive and rehabilitation strategies for motor-impaired users. Although a variety of decoding methods have been proposed for limb trajectory reconstruction, there does not yet exist a review that covers the performance evaluation of these decoding methods. To alleviate this vacancy, in this paper, we evaluate EEG-based limb trajectory decoding methods regarding their advantages and disadvantages from a variety of perspectives. Specifically, we first introduce the differences in motor execution and motor imagery in limb trajectory reconstruction with different spaces (2D and 3D). Then, we discuss the limb motion trajectory reconstruction methods including experiment paradigm, EEG pre-processing, feature extraction and selection, decoding methods, and result evaluation. Finally, we expound on the open problem and future outlooks

Desarrollo de una interfaz cerebro-ordenador orientada al control domótico mediante realidad virtual

La posibilidad de controlar dispositivos con la mente ha fascinado a la humanidad durante el último siglo. Los recientes avances en la neurociencia han contribuido al desarrollo de las primeras interfaces cerebro-ordenador (BCI), que son capaces de decodificar las intenciones de los usuarios en comandos externos a través de sus señales cerebrales. Esta tecnología unida a la realidad virtual (RV) tiene un gran potencial de desarrollo, ya que permite interactuar con dispositivos externos a través escenarios inmersivos. Esto podría mejorar la calidad de vida y la autonomía de las personas con discapacidad motora, al permitirles el control de dispositivos externos a través de las señales cerebrales. Siguiendo estas líneas, en este trabajo se realiza el diseño, el desarrollo y la evaluación de un sistema BCI, basado en señales de control c-VEPs, en un entorno virtual con el fin de controlar un reproductor de música. El diseño y desarrollo se implementa a través de los programas Unity y MEDUSA©. En cuanto a su evaluación, ésta se llevó a cabo con cinco sujetos de control y se obtuvieron unos resultados que muestran que el sistema desarrollado es fácil de usar, rápido y tiene una alta precisión (92,85%), que supera la de otros estudios realizados anteriormente.The ability to control devices with the mind has intrigued mankind for the last century. Recent progress in neuroscience has contributed to the development of the first brain-computer interfaces (BCI), which are able to decode users' intentions into external commands through their brain signals. This technology coupled with virtual reality (VR) has great growth potential as it allows interaction with external devices through immersive scenarios. This could improve the quality of life and the autonomy of motor-disabled people by allowing them to control external devices through their brain signals. Following these guidelines, the present study designs, develops and evaluates a BCI system, based on the control signals c-VEPs, in a virtual environment with the aim to control a music player. The design and development are implemented using Unity and MEDUSA© software. The evaluation was carried out with five control subjects and the results obtained show that the developed system is easy to use, fast and achieved a high accuracy (92.85%), which overcomes that of previous studies.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaMáster en Ingeniería Industria

Brain-Computer Interfaces for Non-clinical (Home, Sports, Art, Entertainment, Education, Well-being) Applications

HCI researchers interest in BCI is increasing because the technology industry is expanding into application areas where efficiency is not the main goal of concern. Domestic or public space use of information and communication technology raise awareness of the importance of affect, comfort, family, community, or playfulness, rather than efficiency. Therefore, in addition to non-clinical BCI applications that require efficiency and precision, this Research Topic also addresses the use of BCI for various types of domestic, entertainment, educational, sports, and well-being applications. These applications can relate to an individual user as well as to multiple cooperating or competing users. We also see a renewed interest of artists to make use of such devices to design interactive art installations that know about the brain activity of an individual user or the collective brain activity of a group of users, for example, an audience. Hence, this Research Topic also addresses how BCI technology influences artistic creation and practice, and the use of BCI technology to manipulate and control sound, video, and virtual and augmented reality (VR/AR)