Imagined speech can be decoded from low- and cross-frequency intracranial EEG features

Reconstructing intended speech from neural activity using brain-computer interfaces holds great promises for people with severe speech production deficits. While decoding overt speech has progressed, decoding imagined speech has met limited success, mainly because the associated neural signals are weak and variable compared to overt speech, hence difficult to decode by learning algorithms. We obtained three electrocorticography datasets from 13 patients, with electrodes implanted for epilepsy evaluation, who performed overt and imagined speech production tasks. Based on recent theories of speech neural processing, we extracted consistent and specific neural features usable for future brain computer interfaces, and assessed their performance to discriminate speech items in articulatory, phonetic, and vocalic representation spaces. While high-frequency activity provided the best signal for overt speech, both low- and higher-frequency power and local cross-frequency contributed to imagined speech decoding, in particular in phonetic and vocalic, i.e. perceptual, spaces. These findings show that low-frequency power and cross-frequency dynamics contain key information for imagined speech decoding.

Are Brain–Computer Interfaces Feasible With Integrated Photonic Chips?

The present paper examines the viability of a radically novel idea for brain–computer interface (BCI), which could lead to novel technological, experimental, and clinical applications. BCIs are computer-based systems that enable either one-way or two-way communication between a living brain and an external machine. BCIs read-out brain signals and transduce them into task commands, which are performed by a machine. In closed loop, the machine can stimulate the brain with appropriate signals. In recent years, it has been shown that there is some ultraweak light emission from neurons within or close to the visible and near-infrared parts of the optical spectrum. Such ultraweak photon emission (UPE) reflects the cellular (and body) oxidative status, and compelling pieces of evidence are beginning to emerge that UPE may well play an informational role in neuronal functions. In fact, several experiments point to a direct correlation between UPE intensity and neural activity, oxidative reactions, EEG activity, cerebral blood flow, cerebral energy metabolism, and release of glutamate. Therefore, we propose a novel skull implant BCI that uses UPE. We suggest that a photonic integrated chip installed on the interior surface of the skull may enable a new form of extraction of the relevant features from the UPE signals. In the current technology landscape, photonic technologies are advancing rapidly and poised to overtake many electrical technologies, due to their unique advantages, such as miniaturization, high speed, low thermal effects, and large integration capacity that allow for high yield, volume manufacturing, and lower cost. For our proposed BCI, we are making some very major conjectures, which need to be experimentally verified, and therefore we discuss the controversial parts, feasibility of technology and limitations, and potential impact of this envisaged technology if successfully implemented in the future.

The Layer 7 Cortical Interface: A Scalable and Minimally Invasive Brain-Computer Interface Platform

Progress toward the development of brain-computer interfaces has signaled the potential to restore, replace, or augment lost or impaired neurological function in a variety of disease states. Existing brain-computer interfaces rely on invasive surgical procedures or brain-penetrating electrodes, which limit addressable applications of the technology and the number of eligible patients. Here we describe a novel approach to constructing a neural interface, comprising conformable thin-film electrode arrays and a minimally invasive surgical delivery system that together facilitate communication with large portions of the cortical surface in bidirectional fashion (enabling both recording and stimulation). We demonstrate the safety and feasibility of rapidly delivering reversible implants containing over 2,000 microelectrodes to multiple functional regions in both hemispheres of the Gottingen minipig brain simultaneously, without requiring a craniotomy, at an effective insertion rate faster than 40 ms per channel, without damaging the cortical surface. We further demonstrate the performance of this system for high-density neural recording, focal cortical stimulation, and accurate neural decoding. Such a system promises to accelerate efforts to better decode and encode neural signals, and to expand the patient population that could benefit from neural interface technology.

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.

Populist Human-Computer Interface

With advances in HCI and AI, and increasing prevalence of commercial social robots and chatbots, humans are communicating with computer interfaces for various applications in a wide range of settings. Kissenger is designed to bring HCI to the populist masses. In order to investigate the role of robotic kissing using the Kissenger device in HCI, the authors conducted a modified version of the imitation game described by Alan Turing by including the use of the kissing machine. Results show that robotic kissing has no effect on the winning rates of the male and female players during human-human communication, but it increases the winning rate of the female player when a chatbot is involved in the game.

The Age of the Cyborg

Following the historical and philosophical grounding, this chapter draws our attention to the question of what it means to be living in a cyborg era, presents the existential and ontological significance of cybernetics, and describes in detail the known forms of cyborgization. This chapter covers the key considerations including conceptualizations of cyborgs, replacement procedures, normalization procedures, improvement procedures, reshaping procedure, and types of cyborgs (animo cyborgs, homo cyborgs, cyber cyborg, and robo cyborgs). It also deals with targeted areas of human-cyborg enhancement, including bionic arms, bionic fingers, bionic legs, bionic ear, bionic eyes, bionic hearts, bionic skin, bionic tongues, bionic brains, and bionic clothes. The importance of computer interfaces and key applications of cyborg research are described throughout the subsection of this chapter, such as Kevin Warwick's and Stelarc cyborg projects, the popularisation of cyborgs in art, Steve Mann's wearable computers, augmented reality goggles, and robo-cyborgs.