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"Brain-computer interfaces"
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Four ethical priorities for neurotechnologies and AI
Current BCI technology is mainly focused on therapeutic outcomes, such as helping people with spinal-cord injuries. It might take years or even decades until BCI and other neurotechnologies are part of our daily lives. Such advances could revolutionize the treatment of many conditions, from brain injury and paralysis to epilepsy and schizophrenia, and transform human experience for the better. But the technology could also exacerbate social inequalities and offer corporations, hackers, governments or anyone else new ways to exploit and manipulate people.
Journal Article
Stable Decoding from a Speech BCI Enables Control for an Individual with ALS without Recalibration for 3 Months
Brain‐computer interfaces (BCIs) can be used to control assistive devices by patients with neurological disorders like amyotrophic lateral sclerosis (ALS) that limit speech and movement. For assistive control, it is desirable for BCI systems to be accurate and reliable, preferably with minimal setup time. In this study, a participant with severe dysarthria due to ALS operates computer applications with six intuitive speech commands via a chronic electrocorticographic (ECoG) implant over the ventral sensorimotor cortex. Speech commands are accurately detected and decoded (median accuracy: 90.59%) throughout a 3‐month study period without model retraining or recalibration. Use of the BCI does not require exogenous timing cues, enabling the participant to issue self‐paced commands at will. These results demonstrate that a chronically implanted ECoG‐based speech BCI can reliably control assistive devices over long time periods with only initial model training and calibration, supporting the feasibility of unassisted home use.
A speech brain‐computer interface enables a clinical‐trial participant with amyotrophic lateral sclerosis to control a communication board and home devices without model retraining or baseline calibration. Six intuitive speech commands are accurately detected and decoded (median accuracy: 90.59%) from a chronic electrocorticographic (ECoG) implant over the ventral sensorimotor cortex throughout a 3‐month study period.
Journal Article
Brain art : brain-computer interfaces for artistic expression
\"This is the first book on brain-computer interfaces (BCI) that aims to explain how these BCI interfaces can be used for artistic goals. Devices that measure changes in brain activity in various regions of our brain are available and they make it possible to investigate how brain activity is related to experiencing and creating art. Brain activity can also be monitored in order to find out about the affective state of a performer or bystander and use this knowledge to create or adapt an interactive multi-sensorial (audio, visual, tactile) piece of art. Making use of the measured affective state is just one of the possible ways to use BCI for artistic expression. We can also stimulate brain activity. It can be evoked externally by exposing our brain to external events, whether they are visual, auditory, or tactile. Knowing about the stimuli and the effect on the brain makes it possible to translate such external stimuli to decisions and commands that help to design, implement, or adapt an artistic performance, or interactive installation. Stimulating brain activity can also be done internally. Brain activity can be voluntarily manipulated and changes can be translated into computer commands to realize an artistic vision. The chapters in this book have been written by researchers in human-computer interaction, brain-computer interaction, neuroscience, psychology and social sciences, often in cooperation with artists using BCI in their work. It is the perfect book for those seeking to learn about brain-computer interfaces used for artistic applications.\"--Page 4 of cover.
Book
Soft, curved electrode systems capable of integration on the auricle as a persistent brain–computer interface
Recent advances in electrodes for noninvasive recording of electroencephalograms expand opportunities collecting such data for diagnosis of neurological disorders and brain–computer interfaces. Existing technologies, however, cannot be used effectively in continuous, uninterrupted modes for more than a few days due to irritation and irreversible degradation in the electrical and mechanical properties of the skin interface. Here we introduce a soft, foldable collection of electrodes in open, fractal mesh geometries that can mount directly and chronically on the complex surface topology of the auricle and the mastoid, to provide high-fidelity and long-term capture of electroencephalograms in ways that avoid any significant thermal, electrical, or mechanical loading of the skin. Experimental and computational studies establish the fundamental aspects of the bending and stretching mechanics that enable this type of intimate integration on the highly irregular and textured surfaces of the auricle. Cell level tests and thermal imaging studies establish the biocompatibility and wearability of such systems, with examples of high-quality measurements over periods of 2 wk with devices that remain mounted throughout daily activities including vigorous exercise, swimming, sleeping, and bathing. Demonstrations include a text speller with a steady-state visually evoked potential-based brain–computer interface and elicitation of an event-related potential (P300 wave).
Significance Conventional electroencephalogram (EEG) recording systems, particularly the hardware components that form the physical interfaces to the head, have inherent drawbacks that limit the widespread use of continuous EEG measurements for medical diagnostics, sleep monitoring, and cognitive control. Here we introduce soft electronic constructs designed to intimately conform to the complex surface topology of the auricle and the mastoid, to provide long-term, high-fidelity recording of EEG data. Systematic studies reveal key aspects of the extreme levels of bending and stretching that are involved in mounting on these surfaces. Examples in persistent brain–computer interfaces, including text spellers with steady-state visually evoked potentials and event-related potentials, with viable operation over periods of weeks demonstrate important advances over alternative brain–computer interface technologies.
Journal Article
Wired Emotions: Ethical Issues of Affective Brain–Computer Interfaces
Ethical issues concerning brain–computer interfaces (BCIs) have already received a considerable amount of attention. However, one particular form of BCI has not received the attention that it deserves: Affective BCIs that allow for the detection and stimulation of affective states. This paper brings the ethical issues of affective BCIs in sharper focus. The paper briefly reviews recent applications of affective BCIs and considers ethical issues that arise from these applications. Ethical issues that affective BCIs share with other neurotechnologies are presented and ethical concerns that are specific to affective BCIs are identified and discussed.
Journal Article
The future of the mind : the scientific quest to understand, enhance, and empower the mind
In this extraordinary, often mind-boggling exploration of the frontiers of neuroscience, Dr. Kaku looks toward the day when we may achieve the ability to upload the human brain to a computer, neuron for neuron; project thoughts and emotions around the world on a brain-net; take a \"smart pill\" to enhance cognition; send our consciousness across the universe; and push the very limits of immortality.
Book
Feature Selection for Motor Imagery EEG Classification Based on Firefly Algorithm and Learning Automata
Motor Imagery (MI) electroencephalography (EEG) is widely studied for its non-invasiveness, easy availability, portability, and high temporal resolution. As for MI EEG signal processing, the high dimensions of features represent a research challenge. It is necessary to eliminate redundant features, which not only create an additional overhead of managing the space complexity, but also might include outliers, thereby reducing classification accuracy. The firefly algorithm (FA) can adaptively select the best subset of features, and improve classification accuracy. However, the FA is easily entrapped in a local optimum. To solve this problem, this paper proposes a method of combining the firefly algorithm and learning automata (LA) to optimize feature selection for motor imagery EEG. We employed a method of combining common spatial pattern (CSP) and local characteristic-scale decomposition (LCD) algorithms to obtain a high dimensional feature set, and classified it by using the spectral regression discriminant analysis (SRDA) classifier. Both the fourth brain–computer interface competition data and real-time data acquired in our designed experiments were used to verify the validation of the proposed method. Compared with genetic and adaptive weight particle swarm optimization algorithms, the experimental results show that our proposed method effectively eliminates redundant features, and improves the classification accuracy of MI EEG signals. In addition, a real-time brain–computer interface system was implemented to verify the feasibility of our proposed methods being applied in practical brain–computer interface systems.
Journal Article