Role of 5G Communication Along With Blockchain Security in Brain-Computer Interfacing

The electroencephalogram is used in brain-computer interface (BCI) in which signal from the human brain is sensed with the help of EEG and then sent to the computer to control the external device without having any touch of muscular body parts. On the other hand, the brain chip interfacing (BCHIs) is a microelectronic chip that has physical connections with the neurons for the transfer of information. The BCI needs a reliable, high-speed network and new security tool that can assist BCI technology. 5G network and blockchain technology is ideal to support the growing needs of brain chip interfacing. Further, the Cloudmind, which is an emerging application of BCI, can be conceptualized by using blockchain technology. In this chapter, brain-computer interfaces (BCIs) are expedient to bridge the connectivity chasm between human and machine (computer) systems via 5G technologies, which offers minimal latency, faster speeds, and stronger bandwidth connectivity with strong cryptographic qualities of blockchain technologies.

A Brief Study of Binaural Beat: A Means of Brain-Computer Interfacing

The human brain tends to follow a rhythm. Sound has a significant impact on our physical and mental health. This sound technology uses binaural beat by generating two tones of marginally different frequencies in each individual ear to facilitate the improved focus of attention, emotion, calming, and sensory organization. Binaural beat helps in memory boosting, relaxation, and work performance. Again because of hearing a binaural beat sound, brainwave stimuli can be diagnosed to pick up a person’s sensitive information. Using this technology in brain-computer interfacing, it is possible to establish a communication between the brain and the computer. Thus, it enables us to go beyond our potential. The aim of this study is to assess the impact and explore the potential contribution of binaural beat to enhancement of human brain performance.

Explorations of A Real-Time VR Emotion Prediction System Using Wearable Brain-Computer Interfacing

The following research describes the potential of using a four-class emotion classification using a four-channel wearable EEG headset combined with VR for evoking emotions from each individual. Multiple researchers have conducted and established emotion recognition by using a 2-D monitor screen for stimulus responses but this introduces artifacts such as the lack of concentration on-screen or external noise disturbance and the bulky and cumbersome wires on an EEG device were difficult and time-consuming to set up thus restricting to only the trained professionals to operate this complex and sensitive medical equipment. Therefore, using a small and portable EEG headset where it was accessible for consumers was used for the brainwave signal collection. The wearable EEG headset collects the brainwave samples at 256Hz at specific locations of the brain (Tp9, Tp10, AF7, AF8) and samples were transformed via FFT to obtain the five bands (Delta, Theta, Alpha, Beta, Gamma) and were classified using random forest classifier. An emotion prediction system was then developed and the trained model was used to benchmark the 4-class emotion prediction accuracy from each individual using a 4-channel low-cost EEG headset. Subsequently, a real-time prediction system was implemented and tested. Early findings showed that it could achieve predictions as high as 76.50% for intra-subject classification results.

Brain–Computer Interfacing Using Functional Near-Infrared Spectroscopy (fNIRS)

Functional Near-Infrared Spectroscopy (fNIRS) is a wearable optical spectroscopy system originally developed for continuous and non-invasive monitoring of brain function by measuring blood oxygen concentration. Recent advancements in brain–computer interfacing allow us to control the neuron function of the brain by combining it with fNIRS to regulate cognitive function. In this review manuscript, we provide information regarding current advancement in fNIRS and how it provides advantages in developing brain–computer interfacing to enable neuron function. We also briefly discuss about how we can use this technology for further applications.

Evolutionary Multitasking-Based Multiobjective Optimization Algorithm for Channel Selection in Hybrid Brain Computer Interfacing Systems

Hybrid-modality brain-computer Interfaces (BCIs), which combine motor imagery (MI) bio-signals and steady-state visual evoked potentials (SSVEPs), has attracted wide attention in the research field of neural engineering. The number of channels should be as small as possible for real-life applications. However, most of recent works about channel selection only focus on either the performance of classification task or the effectiveness of device control. Few works conduct channel selection for MI and SSVEP classification tasks simultaneously. In this paper, a multitasking-based multiobjective evolutionary algorithm (EMMOA) was proposed to select appropriate channels for these two classification tasks at the same time. Moreover, a two-stage framework was introduced to balance the number of selected channels and the classification accuracy in the proposed algorithm. The experimental results verified the feasibility of multiobjective optimization methodology for channel selection of hybrid BCI tasks.