Brain correlates of task-load and dementia elucidation with tensor machine learning using oddball BCI paradigm

Dementia in the elderly has recently become the most usual cause of cognitive decline. The proliferation of dementia cases in aging societies creates a remarkable economic as well as medical problems in many communities worldwide. A recently published report by The World Health Organization (WHO) estimates that about 47 million people are suffering from dementia-related neurocognitive declines worldwide. The number of dementia cases is predicted by 2050 to triple, which requires the creation of an AI-based technology application to support interventions with early screening for subsequent mental wellbeing checking as well as preservation with digital-pharma (the so-called beyond a pill) therapeutical approaches. We present an attempt and exploratory results of brain signal (EEG) classification to establish digital biomarkers for dementia stage elucidation. We discuss a comparison of various machine learning approaches for automatic event-related potentials (ERPs) classification of a high and low task-load sound stimulus recognition. These ERPs are similar to those in dementia. The proposed winning method using tensor-based machine learning in a deep fully connected neural network setting is a step forward to develop AI-based approaches for a subsequent application for subjective- and mild-cognitive impairment (SCI and MCI) diagnostics.Comment: In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8578-8582, May 201

A Novel Brain Computer Interface Design

A brain computer interface (BCI) is a system which connects neural signals to a computer system. They have been used for controlling systems including robotics, on-screen computer control such as mouse movement, typing, and synthesizing audio signals. Invasive, or implanted, systems are often long-term medical solutions, or used for research where very clear signal is required. Non-invasive systems usually rely on exterior signals gathered through a headset using one or more electrode sensors. These signals are composed of sums of neuron activation potentials from brain activity and can be used to determine particular aspects of brain function. All BCIs rely on neural feedback of some form; the most effective systems combine multiple forms of feedback. The proposed BCI design will control audio playback through a connection to a non-invasive EEG sensor headset worn by the user. This design will take a conclusive step toward better integration of this technology for more effective control of systems in the future. It would be helpful to any consumer who wants to control media play back hands free, as well as patients unable to use hands for control. Its advantages include a low price point and simple implementation compared to medical and research grade equipment. In summary, the proposed BCI audio playback control system is a simple yet vital step toward a truly robust implementation of such a system on a larger scale

Assessing Command-Following and Communication With Vibro-Tactile P300 Brain-Computer Interface Tools in Patients With Unresponsive Wakefulness Syndrome

Persons diagnosed with disorders of consciousness (DOC) typically suffer from motor disablities, and thus assessing their spared cognitive abilities can be difficult. Recent research from several groups has shown that non-invasive brain-computer interface (BCI) technology can provide assessments of these patients' cognitive function that can supplement information provided through conventional behavioral assessment methods. In rare cases, BCIs may provide a binary communication mechanism. Here, we present results from a vibrotactile BCI assessment aiming at detecting command-following and communication in 12 unresponsive wakefulness syndrome (UWS) patients. Two different paradigms were administered at least once for every patient: (i) VT2 with two vibro-tactile stimulators fixed on the patient's left and right wrists and (ii) VT3 with three vibro-tactile stimulators fixed on both wrists and on the back. The patients were instructed to mentally count either the stimuli on the left or right wrist, which may elicit a robust P300 for the target wrist only. The EEG data from −100 to +600 ms around each stimulus were extracted and sub-divided into 8 data segments. This data was classified with linear discriminant analysis (using a 10 × 10 cross validation) and used to calibrate a BCI to assess command following and YES/NO communication abilities. The grand average VT2 accuracy across all patients was 38.3%, and the VT3 accuracy was 26.3%. Two patients achieved VT3 accuracy ≥80% and went through communication testing. One of these patients answered 4 out of 5 questions correctly in session 1, whereas the other patient answered 6/10 and 7/10 questions correctly in sessions 2 and 4. In 6 other patients, the VT2 or VT3 accuracy was above the significance threshold of 23% for at least one run, while in 4 patients, the accuracy was always below this threshold. The study highlights the importance of repeating EEG assessments to increase the chance of detecting command-following in patients with severe brain injury. Furthermore, the study shows that BCI technology can test command following in chronic UWS patients and can allow some of these patients to answer YES/NO questions

Application and Prospect of Telesurgery: The Role of Artificial Intelligence

Remote surgery refers to a new surgical mode in which doctors operate on patients with the help of surgical robots, network technology, and virtual reality technology. These robots are located far away from patients. The remote surgical robot system integrates key technologies such as robot, communication technology, remote control technology, space mapping algorithm, and fault tolerance analysis. Apply a variety of emerging networking modes such as 5G, optical fiber private network, fusion network technology, and deterministic network to realize the motion of the subordinate surgical robot and the vision of the main knife, and ensure stable signal transmission and safe remote operation. The development and application of remote surgical robots has become a new trend, which helps to break the barriers of unbalanced regional medical resource allocation, promote the rational allocation of high-quality medical resources, and solve the telemedicine problems in special areas and special circumstances. The development prospect is broad. In the future, relying on the 5G network technology with high speed, low power consumption, and low latency, remote surgery can operate more efficiently and stably, and the surgical robot will also develop toward a more portable and flexible direction, so as to better serve patients