The future of brain–machine interfaces is optical

Ersaro, Nathan Tessema; Yalçın, Cem; Muller, Rikky

doi:10.1038/s41928-023-00926-y

Cited by 8 publications

(6 citation statements)

References 29 publications

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“…Brain-machine interfaces. Brain-machine interfaces (BMIs) [ 190 ] are poised to play a crucial role in the diagnosis of neurological disorders in the future. BMIs, through direct interaction with signals from the brain, hold the potential to identify diseases related to the nervous system, such as Parkinson’s disease or stroke.…”

Section: Challenges and Future Workmentioning

confidence: 99%

A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis

Xu,

Li,

Zhu

et al. 2024

Bioengineering

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Disease diagnosis represents a critical and arduous endeavor within the medical field. Artificial intelligence (AI) techniques, spanning from machine learning and deep learning to large model paradigms, stand poised to significantly augment physicians in rendering more evidence-based decisions, thus presenting a pioneering solution for clinical practice. Traditionally, the amalgamation of diverse medical data modalities (e.g., image, text, speech, genetic data, physiological signals) is imperative to facilitate a comprehensive disease analysis, a topic of burgeoning interest among both researchers and clinicians in recent times. Hence, there exists a pressing need to synthesize the latest strides in multi-modal data and AI technologies in the realm of medical diagnosis. In this paper, we narrow our focus to five specific disorders (Alzheimer’s disease, breast cancer, depression, heart disease, epilepsy), elucidating advanced endeavors in their diagnosis and treatment through the lens of artificial intelligence. Our survey not only delineates detailed diagnostic methodologies across varying modalities but also underscores commonly utilized public datasets, the intricacies of feature engineering, prevalent classification models, and envisaged challenges for future endeavors. In essence, our research endeavors to contribute to the advancement of diagnostic methodologies, furnishing invaluable insights for clinical decision making.

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Section: Challenges and Future Workmentioning

confidence: 99%

A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis

Xu,

Li,

Zhu

et al. 2024

Bioengineering

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“… 46 Thus future BMIs are likely to be optics-based. 47 We will begin by outlining the experiments that have been realized with the current two-photon BMI (2p-BMI).…”

Section: Brain Machine Interfaces Using In Vivo Mu...mentioning

confidence: 99%

“…If such a system could learn to provide appropriate feedback reflecting the brain state in real time, it could suppress epileptic seizures and replace deep brain stimulation for treating Parkinson’s disease. 47 Moreover, it could also extend the function of the brain, as foretold in science fiction novels.…”

Section: Interaction Of Brain and Machinementioning

confidence: 99%

Closed-loop experiments and brain machine interfaces with multiphoton microscopy

Hira

2024

Neurophoton.

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. In the field of neuroscience, the importance of constructing closed-loop experimental systems has increased in conjunction with technological advances in measuring and controlling neural activity in live animals. We provide an overview of recent technological advances in the field, focusing on closed-loop experimental systems where multiphoton microscopy—the only method capable of recording and controlling targeted population activity of neurons at a single-cell resolution in vivo —works through real-time feedback. Specifically, we present some examples of brain machine interfaces (BMIs) using in vivo two-photon calcium imaging and discuss applications of two-photon optogenetic stimulation and adaptive optics to real-time BMIs. We also consider conditions for realizing future optical BMIs at the synaptic level, and their possible roles in understanding the computational principles of the brain.

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“…For instance, how should the treating therapist characterize her confidential relationship with Sarah in this context, and are there novel or difficult-to-define harms that she may be subject to? If the rudimentary telepathic connection Sarah and her partner share advances further, in line with recent brain-to-brain interfaces, 11 would the therapist be treating with Sarah, or Sarah and her (physically absent) partner? More radically, how much of Sarah's sensory, cognitive, and emotional information does she need to directly share with her partner before her mind becomes-in some clinically relevant sense-merged with her partner's mind?…”

Section: Introductionmentioning

confidence: 96%

When Two Become One: Singular Duos and the Neuroethical Frontiers of Brain-to-Brain Interfaces

Zohny,

Savulescu

2024

Camb Q Healthc Ethics

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Advances in brain–brain interface technologies raise the possibility that two or more individuals could directly link their minds, sharing thoughts, emotions, and sensory experiences. This paper explores conceptual and ethical issues posed by such mind-merging technologies in the context of clinical neuroethics. Using hypothetical examples along a spectrum from loosely connected pairs to fully merged minds, the authors sketch out a range of factors relevant to identifying the degree of a merger. They then consider potential new harms like loss of identity, psychological domination, loss of mental privacy, and challenges for notions of autonomy and patient benefit when applied to merged minds. While radical technologies may seem to necessitate new ethical paradigms, the authors suggest the individual-focus underpinning clinical ethics can largely accommodate varying degrees of mind mergers so long as individual patient interests remain identifiable. However, advanced decisionmaking and directives may have limitations in addressing the dilemmas posed. Overall, mind-merging possibilities amplify existing challenges around loss of identity, relating to others, autonomy, privacy, and the delineation of patient interests. This paper lays the groundwork for developing resources to address the novel issues raised, while suggesting the technologies reveal continuity with current healthcare ethics tensions.

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The future of brain–machine interfaces is optical

Cited by 8 publications

References 29 publications

A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis

A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis

Closed-loop experiments and brain machine interfaces with multiphoton microscopy

When Two Become One: Singular Duos and the Neuroethical Frontiers of Brain-to-Brain Interfaces

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