Electroencephalogram based communication system for locked in state person using mentally spelled tasks with optimized network model

A review on the performance of brain-computer interface systems used for patients with locked-in and completely locked-in syndrome

Patients with locked-in syndrome (LIS) and complete locked-in syndrome (CLIS) own a fully functional brain restricted within a non-functional body. In order to help LIS patients stay connected with their surroundings, brain-computer interfaces (BCIs) and related technologies have emerged. BCIs translate brain activity into actions that can be performed by external devices enabling LIS patients to communicate, leading to an increase in their quality of life. The past decade has seen the rapid development of BCIs that have the potential to be used for patients with locked-in syndrome, from which a great deal is tested only on healthy subjects and not on actual patients. This study aims to (1) provide the readers with a comprehensive study that contributes to this growing area of research by exploring the performance of BCIs tested specifically on LIS and CLIS patients, (2) give an overview of different modalities and paradigms used in different stages of the locked-in syndrome, and (3) discuss the contributions and limitations of BCIs introduced for the LIS and CLIS patients in the state-of-the-art and lay a groundwork for researchers interested in this field.

Locked-in syndrome revisited

The locked-in syndrome (LiS) is characterized by quadriplegia with preserved vertical eye and eyelid movements and retained cognitive abilities. Subcategorization, aetiologies and the anatomical foundation of LiS are discussed. The damage of different structures in the pons, mesencephalon and thalamus are attributed to symptoms of classical, complete and incomplete LiS and the locked-in plus syndrome, which is characterized by additional impairments of consciousness, making the clinical distinction to other chronic disorders of consciousness at times difficult. Other differential diagnoses are cognitive motor dissociation (CMD) and akinetic mutism. Treatment options are reviewed and an early, interdisciplinary and aggressive approach, including the provision of psychological support and coping strategies is favoured. The establishment of communication is a main goal of rehabilitation. Finally, the quality of life of LiS patients and ethical implications are considered. While patients with LiS report a high quality of life and well-being, medical professionals and caregivers have largely pessimistic perceptions. The negative view on life with LiS must be overthought and the autonomy and dignity of LiS patients prioritized. Knowledge has to be disseminated, diagnostics accelerated and technical support system development promoted. More well-designed research but also more awareness of the needs of LiS patients and their perception as individual persons is needed to enable a life with LiS that is worth living.

Eye Movement Signal Classification for Developing Human-Computer Interface Using Electrooculogram

Human-computer interfaces (HCI) allow people to control electronic devices, such as computers, mouses, wheelchairs, and keyboards, by bypassing the biochannel without using motor nervous system signals. These signals permit communication between people and electronic-controllable devices. This communication is due to HCI, which facilitates lives of paralyzed patients who do not have any problems with their cognitive functioning. The major plan of this study is to test out the feasibility of nine states of HCI by using modern techniques to overcome the problem faced by the paralyzed. Analog Digital Instrument T26 with a five-electrode system was used in this method. Voluntarily twenty subjects participated in this study. The extracted signals were preprocessed by applying notch filter with a range of 50 Hz to remove the external interferences; the features were extracted by applying convolution theorem. Afterwards, extracted features were classified using Elman and distributed time delay neural network. Average classification accuracy with 90.82% and 90.56% was achieved using two network models. The accuracy of the classifier was analyzed by single-trial analysis and performances of the classifier were observed using bit transfer rate (BTR) for twenty subjects to check the feasibility of designing the HCI. The achieved results showed that the ERNN model has a greater potential to classify, identify, and recognize the EOG signal compared with distributed time delay network for most of the subjects. The control signal generated by classifiers was applied as control signals to navigate the assistive devices such as mouse, keyboard, and wheelchair activities for disabled people.

Construction and 3D Simulation of Virtual Animation Instant Network Communication System Based on Convolution Neural Networks

In recent years, great progress has been made in 3D simulation modeling of instant network communication system, such as the application of virtual reality technology and 3D virtual animation online modeling technology. Facing the increasing demand of different industries, how to build an instant network communication system for 3D virtual animation has become a research hotspot. On this basis, the construction method of fast instant network communication system based on convolutional neural network and fusion morphological 3D simulation model is studied. This paper analyzes the research status of instant network communication system. The experiment optimizes and improves the shortcomings of the current research hotspot of virtual animation instant network communication system and takes the morphological 3D simulation model fusion as the core for in-depth optimization. Finally, the experimental results show that the fusion morphological 3D simulation model can reconstruct the standard 3D virtual animation model according to different needs and can quickly model the optimization strategy according to the local differences of different animations. The response accuracy of the network communication system reaches 97.7%.