Neurosciences and Wireless Networks: The Potential of Brain-Type Communications and Their Applications

Abstract

This paper presents the first comprehensive tutorial on a promising research field located at the frontier of two wellestablished domains, neurosciences and wireless communications, motivated by the ongoing efforts to define the Sixth Generation of Mobile Networks (6G). In particular, this tutorial first provides a novel integrative approach that bridges the gap between these two seemingly disparate fields. Then, we present the state-ofthe-art and key challenges of these two topics. In particular, we propose a novel systematization that divides the contributions into two groups, one focused on what neurosciences will offer to future wireless technologies in terms of new applications and systems architecture (Neurosciences for Wireless Networks), and the other on how wireless communication theory and nextgeneration wireless systems can provide new ways to study the brain (Wireless Networks for Neurosciences). For the first group, we explain concretely how current scientific understanding of the brain would enable new applications within the context of a new type of service that we dub brain-type communications and that has more stringent requirements than human-and machine-type communication. In this regard, we expose the key requirements of brain-type communication services and discuss how future wireless networks can be equipped to deal with such services. Meanwhile, for the second group, we thoroughly explore modern communication systems paradigms, including Internet of Bio-Nano Things and wireless-integrated brainmachine interfaces, in addition to highlighting how complex systems tools can help bridging the upcoming advances of wireless technologies and applications of neurosciences. Brain-controlled vehicles are then presented as our case study to demonstrate for both groups the potential created by the convergence of neurosciences and wireless communications, probably in 6G. In summary, this tutorial is expected to provide a largely missing articulation between neurosciences and wireless communications while delineating concrete ways to move forward in such an interdisciplinary endeavor.

Imaginary Control of a Mobile Vehicle Using Deep Learning Algorithm: A Brain Computer Interface Study

“…In the previous studies [1], [13], the feature extraction step counted as a critical step in the identification methods. The next challenge involves the algorithms for choosing the informative features from a feature pool which contain the main information related to the aim patterns, such as an ERD/ERS wave.…”

“…Neurons have the potential to produce imaginary movement patterns similar to real movement patterns with smaller amplitudes [10]. The investigated topics related to imaginary movement patterns include: controlling a mobile vehicle [11], controlling a prosthetic hand (wearable robots, exoskeleton robots) [12], [13], controlling appliances such as lamps [14], controlling aerial vehicles such as quadcopters [15], [16], control of a vehicle in the main directions [17], break assistant commands also named emergency breaks [18], [19], change lane commands [20] and steering control [11], [21]. In the above-mentioned topics, two considerable issues have been investigated, namely accuracy and time delay.…”

Cyborg Insects: Bug or a Feature?

“…Importantly, a new era of HMI based on brain-type communications [122] is set to benefit the development of cyborg insects, considering the research effort towards wireless neural recording and stimulation technology. One of the most significant features of HMI is the simultaneous access to neuronal signals (both recording and stimulation) and human (animal) behaviour.…”

Brain-computer interfaces in safety and security fields: Risks and applications

“…In the sensing process, the bio-electric signals are sensed through several technologies that can be classified as invasive and non-invasive (Bonci et al, 2021). The most used non-invasive BCIs are electroencephalography (EEG) (Li et al, 2015;Moioli et al, 2021), and the most used invasive BCIs are local field potentials (LFP) and electrocorticography (ECoG) (Moioli et al, 2021). Other possible sensors include functional magnetic resonance imaging (fMRI) systems, nearinfrared (NIR) systems, magnetocencephalography (MEG), and microelectrode-based intracortical neurophysiology (L. R. Hochberg & J. P. Donoghue, 2006).…”