Cybersecurity in neural interfaces: Survey and future trends

Jiang, Xinyu; Fan, Jiahao; Zhu, Ziyue; Wang, Zihao; Guo, Yao; Liu, Xiangyu; Jia, Fumin; Dai, Chenyun

doi:10.1016/j.compbiomed.2023.107604

Cited by 3 publications

(3 citation statements)

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“…The cumbersome connection method and immovable desktop machine limit the mobility of the system, restricting the synchronized application of fNIRS and EEG [17]. Several key factors need to be optimized in the design: (1) Contemporary emotion recognition approaches often overlook real-time dynamic changes in emotional states, emphasizing static, discrete emotional states [18]. This necessitates advanced signal processing techniques to improve the accuracy and speed of emotion recognition by leveraging the temporal dynamics of fNIRS-EEG signals.…”

Section: Research Gapmentioning

confidence: 99%

“…where C is the concentration of the target substance (HbO or HbR), 0 I is the intensity of the incident light, I is the intensity of the light detected after transmitting through the sample, ε is the molar absorption coefficient of the target substance, and d is the optical distance, i.e., the distance the light travels through the sample. To filter out motion artifacts, we applied Spline interpolation and Savitzky-Golay filtering techniques, mitigating alterations in optical signals caused by participant movements, and thereby en- (1) where C is the concentration of the target substance (HbO or HbR), I 0 is the intensity of the incident light, I is the intensity of the light detected after transmitting through the sample, ε is the molar absorption coefficient of the target substance, and d is the optical distance, i.e., the distance the light travels through the sample. To filter out motion artifacts, we applied Spline interpolation and Savitzky-Golay filtering techniques, mitigating alterations in optical signals caused by participant movements, and thereby enhancing fNIRS data accuracy [27].…”

Section: Functional Near-infrared Spectroscopy-electroencephalography...mentioning

confidence: 99%

“…The field of human-computer interaction is rapidly advancing with the development of brain-computer interface technologies [1]. Emotion recognition is crucial for interpreting brain signals to discern emotional states.…”

Section: Introduction 1background and Motivationmentioning

confidence: 99%

See 2 more Smart Citations

Temporal Convolutional Network-Enhanced Real-Time Implicit Emotion Recognition with an Innovative Wearable fNIRS-EEG Dual-Modal System

Chen,

Yu,

Wang

et al. 2024

Electronics

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Emotion recognition remains an intricate task at the crossroads of psychology and artificial intelligence, necessitating real-time, accurate discernment of implicit emotional states. Here, we introduce a pioneering wearable dual-modal device, synergizing functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) to meet this demand. The first-of-its-kind fNIRS-EEG ensemble exploits a temporal convolutional network (TC-ResNet) that takes 24 fNIRS and 16 EEG channels as input for the extraction and recognition of emotional features. Our system has many advantages including its portability, battery efficiency, wireless capabilities, and scalable architecture. It offers a real-time visual interface for the observation of cerebral electrical and hemodynamic changes, tailored for a variety of real-world scenarios. Our approach is a comprehensive emotional detection strategy, with new designs in system architecture and deployment and improvement in signal processing and interpretation. We examine the interplay of emotions and physiological responses to elucidate the cognitive processes of emotion regulation. An extensive evaluation of 30 subjects under four emotion induction protocols demonstrates our bimodal system’s excellence in detecting emotions, with an impressive classification accuracy of 99.81% and its ability to reveal the interconnection between fNIRS and EEG signals. Compared with the latest unimodal identification methods, our bimodal approach shows significant accuracy gains of 0.24% for EEG and 8.37% for fNIRS. Moreover, our proposed TC-ResNet-driven temporal convolutional fusion technique outperforms conventional EEG-fNIRS fusion methods, improving the recognition accuracy from 0.7% to 32.98%. This research presents a groundbreaking advancement in affective computing that combines biological engineering and artificial intelligence. Our integrated solution facilitates nuanced and responsive affective intelligence in practical applications, with far-reaching impacts on personalized healthcare, education, and human–computer interaction paradigms.

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Section: Research Gapmentioning

confidence: 99%

Section: Functional Near-infrared Spectroscopy-electroencephalography...mentioning

confidence: 99%