Phase synchrony information plays a crucial role in analyzing functional brain connectivity and identifying brain activities. A widely adopted feature extraction pipeline, composed of preprocessing, selection of EEG acquisition channels, and phase locking value (PLV) calculation, has achieved success in motor imagery classification (MI). However, this pipeline is manual and reliant on expert knowledge, limiting its convenience and adaptability to different application scenarios. Moreover, most studies have employed mediocre data-independent spatial filters to suppress noise, impeding the exploration of more significant phase synchronization phenomena. To address the issues, we propose the concept of phase synchrony component self-organization, which enables the adaptive learning of data-dependent spatial filters for automating both the preprocessing and channel selection procedures. Based on this concept, the first deep learning end-to-end network is developed, which directly extracts phase synchrony-based features from raw EEG signals and perform classification. The network learns optimal filters during training, which are obtained when the network achieves peak classification results. Extensive experiments have demonstrated that our network outperforms state-of-the-art methods. Remarkably, through the learned optimal filters, significant phase synchronization phenomena can be observed. Specifically, by calculating the PLV between a pair of signals extracted from each sample using two of the learned spatial filters, we have obtained an average PLV exceeding 0.87 across all tongue MI samples. This high PLV indicates a groundbreaking discovery in the synchrony pattern of tongue MI.

Ever year, more than a million people in North America suffer some form of spinal cord injury (SCI), with an annual cost of more than $7 billion to treat and rehabilitate those patients. The medical community has made incredible gains toward mitigating, if not reversing, the effects of paralysis in the last quarter-century including advances in pharmacology, stem cell technologies, neuromodulation, and external prosthetics. Electrical stimulation of the spinal cord has already shown especially promising results in helping spinal injury patients rehabilitate, improving not just extremity function but spasticity, bladder and blood pressure control as well. Now, in a study published in Nature Tuesday, SCI therapy startup Onward Medical, announced that it has helped improve a formerly-paraplegic man's walking gait through the use of an implanted brain computer interface (BCI) and novel "digital bridge" that spans the gap where the spine was severed. We've been zapping paraplegic patients' spines with low-voltage jolts as part of their physical rehabilitation for years in a process known as Functional Electrical Stimulation (FES).

During the last ten years there has been growing interest in the develop- ment of Brain Computer Interfaces (BCIs). The field has mainly been driven by the needs of completely paralyzed patients to communicate. With a few exceptions, most human BCIs are based on extracranial elec- troencephalography (EEG). However, reported bit rates are still low. One reason for this is the low signal-to-noise ratio of the EEG [16].

A brain-computer interface (BCI) allows users to "communicate" with a computer without using their muscles. BCI based on sensori-motor rhythms use imaginary motor tasks, such as moving the right or left hand to send control signals. The performances of a BCI can vary greatly across users but also depend on the tasks used, making the problem of appropriate task selection an important issue. This study presents a new procedure to automatically select as fast as possible a discriminant motor task for a brain-controlled button. We develop for this purpose an adaptive algorithm UCB-classif based on the stochastic bandit theory.

Abstract: In this study, we illustrate the progress of BCI research and present scores of unveiled contemporary approaches. First, we explore a decoding natural speech approach that is designed to decode human speech directly from the human brain onto a digital screen introduced by Facebook Reality Lab and University of California San Francisco. Then, we study a recently presented visionary project to control the human brain using Brain-Machine Interfaces (BMI) approach. We also investigate well-known electroencephalography (EEG) based Emotiv Epoc Neuroheadset to identify six emotional parameters including engagement, excitement, focus, stress, relaxation, and interest using brain signals by experimenting the neuroheadset among three human subjects where we utilize two supervised learning classifiers, Naive Bayes and Linear Regression to show the accuracy and competency of the Epoc device and its associated applications in neurotechnological research. We present experimental studies and the demonstration indicates 69% and 62% improved accuracy for the aforementioned classifiers respectively in reading the performance matrices of the participants.

Elon Musk announced his Neuralink is hosting a'show and tell' progress event on October 31, which will be the first progress update since the world watched a brain-chipped monkey play a video game with its mind in April 2021 - this animal later died during testing. The biotech firm is developing a brain-computer interface that it claims could one day make humans hyper-intelligent, and allow paralyzed people to walk again. Musk shared news of the Halloween event on Twitter, no other details were included, but it follows rumors that Neuralink has offered to buy its rival Synchron, which recently completed the first brain-chip in a human. In April Musk shared that Neuralink was moving along to start human trials at the end of 2022, which could very well be what the billionaire has in store at the October presentation. Neuralink showed its first progress update in August 2020 during a demonstration that showcased a pig with an early version of the brain chip.

AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner Can the mind connect directly with #artificialintelligence, #robots and other minds through brain-computer interface technologies to transcend our human limitations? Brain-computer interfaces (BCIs) have shown great prospects as real-time bidirectional links between living brains and actuators. Artificial intelligence (AI), which can advance the analysis and decoding of neural activity, has turbocharged the field of BCI. BCI technology allows a human brain and an external device to talk to one another--to exchange signals. It gives humans the ability to directly control machines, without the physical constraints of the body.

While it is possible for artificial intelligence (AI) to mimic the movements of a human body, it cannot perfectly clone the human brain. Each creature from microbe to man is unique when you consider that every life form is assembled from the same identical building blocks. Every electron in the universe is indistinguishable, by definition. We have entered the fourth industrial revolution, an era that will be defined and driven by the rise of artificial intelligence, extreme automation and ubiquitous connectivity. While human Intelligence looks to adjust to new environments by using a combination of various cognitive processes, AI aims to create machines that can imitate human behavior and perform human-like actions.

A paralysed man has made the first "direct-thought tweet" after having a computer chip implanted in his brain. Philip O-Keefe, a 62-year-old Australian who suffers from amyotrophic lateral sclerosis (ALS), composed and posted the tweet using only his thoughts via a brain computer interface developed by neurotech startup Synchron. I created this tweet just by thinking it," stated the tweet, which was posted to the account of Synchron CEO Thomas Oxley. After sharing the initial tweet, Mr O'Keefe posted seven further tweets replying to questions from Twitter users. "My hope is that I'm paving the way for people to tweet through thoughts," the final one stated. Follow live coverage of Nasa's James Webb Space Telescope launch James Webb Space Telescope successfully launched by Nasa Crypto experts make bitcoin price predictions for 2022 Follow live coverage of Nasa's James Webb Space Telescope launch The Stentrode device was first implanted in April 2020 after Mr O'Keefe's condition deteriorated to a point that he was unable to engage in work-related or other independent activities. It was inserted through the jugular vein in order to avoid invasive brain surgery, and has since allowed him to reconnect with loved ones and colleagues via email, as well as play simple computer-based gamed like Solitaire. "When I first heard about this technology, I knew how much independence it could give back to me," Mr O'Keefe said after posting the tweet, according to a press release from Synchron. "The system is astonishing, it's like learning to ride a bike – it takes practice, but once you're rolling, it becomes natural.

"The skull acts as a bastion of privacy; the brain is the last private part of ourselves," Australian neurosurgeon Tom Oxley says from New York. Oxley is the CEO of Synchron, a neurotechnology company born in Melbourne that has successfully trialled hi-tech brain implants that allow people to send emails and texts purely by thought. In July this year, it became the first company in the world, ahead of competitors like Elon Musk's Neuralink, to gain approval from the US Food and Drug Administration (FDA) to conduct clinical trials of brain computer interfaces (BCIs) in humans in the US. Synchron has already successfully fed electrodes into paralysed patients' brains via their blood vessels. The electrodes record brain activity and feed the data wirelessly to a computer, where it is interpreted and used as a set of commands, allowing the patients to send emails and texts.