Exploring the Intersection of Brain–Computer Interfaces and Quantum Sensing: A Review of Research Progress and Future Trends

Liao, Kun; Yang, Zhaochu; Tao, Dong; Zhao, Libo; Pires, Nuno; Dorao, Carlos Alberto; Stokke, Bjørn Torger; Roseng, Lars Eric; Liu, Wen; Jiang, Zhuangde

doi:10.1002/qute.202300185

Adv Quantum Tech

2023

DOI: 10.1002/qute.202300185

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Exploring the Intersection of Brain–Computer Interfaces and Quantum Sensing: A Review of Research Progress and Future Trends

Kun Liao,

Zhaochu Yang,

Dong Tao

et al.

Abstract: Brain–computer interfaces (BCIs) can revolutionize how humans interact with technology, but several scientific and technological challenges must be addressed to realize their full potential. Recent developments in quantum‐based sensing methods offer promising solutions to some of these challenges. This review provides an overview of the progress, challenges, and prospects of BCIs research and discuss the feasibility of integrating quantum sensor technology in BCI systems. The applications of quantum sensing in… Show more

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2024

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Cited by 2 publications

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Advances in optical recording techniques for non-invasive monitoring of electrophysiological signals

Li,

Ding,

Wang

et al. 2024

J. Phys. D: Appl. Phys.

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The study of electrophysiological signals is crucial for understanding neural functions and physiological processes. Electrophysiological recordings offer direct insights into electrical activity across cellular membranes, aiding in diagnosing and treating neurological disorders. Different from the conventional recording method based on electrical signals and the genetically encoded with fluorescent proteins methods, this review explores label-free mechanisms for optically recording electrophysiological signals: electrochromic materials, surface plasmon resonance (SPR) responses, quantum dots (QDs), and semiconductor-based optoelectronic sensors. The sophistication and limitations of each technology have been discussed, providing insights into potential future directions in this field. Electrochromic materials change optical properties through redox reactions induced by voltages, offering high signal-to-noise ratios and rapid response capabilities. However, these materials have limited biocompatibility and stability. SPR technology modulates signals in response to local changes in electrical potential, achieving high sensitivity. However, challenges such as scattering noise and electro-optic effects still need to be addressed. QDs utilize their photoluminescent properties for high sensitivity and resolution, but concerns about connection efficiency and biocompatibility remain. Semiconductor optoelectronic technologies offer rapid response times, wireless functionality, and integration potential. However, improvements are needed in terms of toxicity, compatibility with biological tissues, and signal amplification and processing. These methods have advantages in neuroscience, medical diagnostics, and biological research, including rapid response, high sensitivity, and label-free monitoring. By combining different optical recording techniques, the performance of voltage imaging can be optimized. In conclusion, interdisciplinary collaboration and innovation are essential for advancing the optical recording of electrophysiological signals and developing diagnostic and therapeutic approaches.

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Advances in optical recording techniques for non-invasive monitoring of electrophysiological signals

Li,

Ding,

Wang

et al. 2024

J. Phys. D: Appl. Phys.

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Optimizing renewable energy systems through artificial intelligence: Review and future prospects

Ukoba,

Olatunji,

Adeoye

et al. 2024

Energy & Environment

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The global transition toward sustainable energy sources has prompted a surge in the integration of renewable energy systems (RES) into existing power grids. To improve the efficiency, reliability, and economic viability of these systems, the synergistic application of artificial intelligence (AI) methods has emerged as a promising avenue. This study presents a comprehensive review of the current state of research at the intersection of renewable energy and AI, highlighting key methodologies, challenges, and achievements. It covers a spectrum of AI utilizations in optimizing different facets of RES, including resource assessment, energy forecasting, system monitoring, control strategies, and grid integration. Machine learning algorithms, neural networks, and optimization techniques are explored for their role in complex data sets, enhancing predictive capabilities, and dynamically adapting RES. Furthermore, the study discusses the challenges faced in the implementation of AI in RES, such as data variability, model interpretability, and real-time adaptability. The potential benefits of overcoming these challenges include increased energy yield, reduced operational costs, and improved grid stability. The review concludes with an exploration of prospects and emerging trends in the field. Anticipated advancements in AI, such as explainable AI, reinforcement learning, and edge computing, are discussed in the context of their potential impact on optimizing RES. Additionally, the paper envisions the integration of AI-driven solutions into smart grids, decentralized energy systems, and the development of autonomous energy management systems. This investigation provides important insights into the current landscape of AI applications in RES.

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Exploring the Intersection of Brain–Computer Interfaces and Quantum Sensing: A Review of Research Progress and Future Trends

Cited by 2 publications

References 191 publications

Advances in optical recording techniques for non-invasive monitoring of electrophysiological signals

Advances in optical recording techniques for non-invasive monitoring of electrophysiological signals

Optimizing renewable energy systems through artificial intelligence: Review and future prospects

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