Using Machine Learning Techniques to Model Encoder/Decoder Pair for Non-invasive Electroencephalographic Wireless Signal Transmission

This study investigated the application and enhancement of Non-Invasive Brain-Computer Interfaces (NI-BCIs), focused on enhancing the efficiency and effectiveness of this technology for individuals with severe physical limitations. The core research goal was to improve current limitations associated with wires, noise, and invasive procedures often associated with BCI technology. The key discussed solution involves developing an optimized Encoder/Decoder (E/D) pair using machine learning techniques, particularly those borrowed from Generative Adversarial Networks (GAN) and other Deep Neural Networks, to minimize data transmission and ensure robustness against data degradation. The study highlighted the crucial role of machine learning in self-adjusting and isolating essential data for accurate and efficient classification. The research design involved training this E/D pair to unlock applications of NI EEG BCIs, such as speech synthesis and seamless control of mobile devices. This research successfully trained the E/D pair with a compression ratio of 500 to 75 data points. With parallel processing, this paper successfully processed and transmitted 36 channels of EEG data without data loss at 97% accuracy in 0.0752s. By successfully developing a robust E/D pair, the study aims to revolutionize BCI technology, paving the way for more intuitive interfaces and significantly improving the quality of life for locked-in individuals. This research thus contributes to advancements in NI-BCIs, harnessing machine learning to address current limitations and unlock new possibilities for this critical technology

Design of a Healthcare Monitoring and Communication System for Locked-In Patients Using Machine Learning, IOTs, and Brain-Computer Interface Technologies

Machine learning (ML) models have shown great promise in advancing brain-computer interface (BCI) signal processing and in enhancing the capabilities of Internet of Things (IoT) mobile devices. By combining these advancements into a comprehensive healthcare monitoring and communication system, we may significantly improve the quality of life for patients living with locked-in syndrome. To that effect, we present a three-tiered approach to systems design using known ML models: data collection, local integrated system deployed on IoT hardware, and administrative management. The first tier focuses on IoT sensors and non-invasive recording of brain signals, their calibration and data collection, and data processing. The second tier focuses on aggregating and directing the data, an alert system for caregivers, and a BCI for personalized communication. The last tier focuses on accountability and essential management tools. This research-in-progress demonstrates the feasibility of integrating current technologies to improve care for locked-in patients

Using Machine Learning Techniques to model Encoder/ Decoder Pair for Non-invasive Electroencephalographic Wireless Signal Transmission

This project demonstrates a proof of concept for developing a means to remove the wires from Electroencephalograph (EEG) Brain to Computer Interface (BCI) systems while maintaining data integrity and increasing the speed of transmission. This paper uses Machine Learning techniques to develop an Encoder/Decoder pair. The Encoder pair learns the important information from the analog signal, reducing the amount of data encoded and transmitted. The Decoder ignores the noise and expands the transmitted data for further processing. This paper uses one channel from an EEG-BCI system and organizes the analog signal in 500 datapoint frames. The Encoder reduces the frames to 75 datapoints and after noise injection, the decoder successfully expands them back to virtually indistinguishable frames from the originals