China
Africa
Explore chapters and articles related to this topic
Bidirectional Neural Interfaces
Published in Chang S. Nam, Anton Nijholt, Fabien Lotte, Brain–Computer Interfaces Handbook, 2018
Mikhail A. Lebedev, Alexei Ossadtchi
Neural decoding is performed by mathematical algorithms, called decoders. Ideally, the decoder design should be based on good understanding on the functional role of the recorded neural signals. In practice, this is possible only to a certain degree because we still know very little about the neural representation and processing of information. Current NIs rely on the existing theories of brain processing, some of them traditional and the others more innovative. In the motor domain, the most traditional view is that motor regions of the brain form a hierarchy and, depending on the hierarchical rankings of the regions involved in controlling a particular behavior, the behavior would be more or less automated (Bernstein 1967). The most automated motor behaviors, such as spinal reflexes (Sherrington 1906) and locomotion generation (Guertin 2009), are controlled by the spinal circuits, somewhat more complex behaviors engage the brainstem, and the most complex voluntary movements are controlled by cortical areas. Many current NIs utilize cortical recordings, so that they can be thought of as NIs mimicking voluntary motor control.
Extended Mind Over Matter: Privacy Protection Is the Sine Qua Non
Published in AJOB Neuroscience, 2023
Consider the example of passive objective monitoring (also: passive sensing). Passive objective monitoring generates data (including metadata) about an agent’s mind via a range of processes in the absence of active user engagement. For instance, smartphone tracking of geolocation, mobility patterns, circadian movement, and other motion-sense data have been shown to detect indicators for Alzheimer’s disease, relapse prediction in schizophrenia, and mental states including stress, anxiety, loneliness, and depression (Cornet and Holden 2018; Ryding and Kuss 2020). Similarly, neurotechnologies generate data about the brain, and by extension the mind, often without requiring active input from subjects (unless lying motionless in a functional magnetic resonance imaging [fMRI] scanner counts). One example is machine learning approaches such as generative adversarial networks (GANs), which have recently been used in neural decoding to produce reconstructions of subjects’ visual stimuli (Dado et al. 2022). Although some neurotechnologies involve user interaction, such as electroencephalogram (EEG) neurofeedback, other kinds do not require bilateral exchange or cognitive task performance and thus cannot be causative of mental data. In essence, they do not fit within the contours of the EMT. Yet these information systems clearly produce something approximating mental data insofar as they reveal intimate details about a person’s inner life.
NeuroEthics and the BRAIN Initiative: Where Are We? Where Are We Going?
Published in AJOB Neuroscience, 2020
Walter J. Koroshetz, Jackie Ward, Christine Grady
The NIH formed a working group to the Advisory Committee to the NIH Director (ACD) to advise on the structure and goals of the BRAIN Initiative at its inception. The report, BRAIN 2025: A Scientific Vision, has served as a forward-looking roadmap for NIH staff in designing this ambitious neuroscience program. The authors of BRAIN 2025 recognized the important need to integrate ethics into the BRAIN Initiative, both by considering issues that are shared with other areas of science and also those unique to the focus on brain circuits. The list of anticipated issues included: (a) the means by which the brain gives rise to consciousness, our innermost thoughts and our most basic human needs; (b) how knowledge of the development of brain circuits could be used to enhance cognitive development in our schools; (c) the circumstances under which mechanistic understanding of addiction and other neuropsychiatric disorders would be used to judge accountability in our legal system or under which objective measurements of pain states in the brain would be used in civil litigation involving damages for pain and suffering; (d) studies of decision‐making used to tailor advertising campaigns; and (e) issues “of privacy of one’s own thoughts and mental processes in an age of increasingly sophisticated neural ‘decoding’ abilities.”
When Thinking is Doing: Responsibility for BCI-Mediated Action
Published in AJOB Neuroscience, 2020
Stephen Rainey, Hannah Maslen, Julian Savulescu
Given challenges in signal acquisition, caused by electrode placement, demands of spatio-temporal resolution, and the dynamics of brain signals, decoding and classification of signals is likely to become more prediction-based, and to use more artificial intelligence. The convergence of predictive neural decoding strategies with BCI technologies has implications for motor prostheses (Truccolo, Hochberg, and Donoghue 2010). Such developments are likely to exacerbate the potential control deficit we are discussing. This is hardly science fiction. Rather, it seems an obvious next step in developing technologies that are robust in decoding fast-changing input.