Modified multidimensional scaling on EEG signals for emotion classification

Garima, .; Goel, Nidhi; Rathee, Neeru

doi:10.1007/s11042-023-14671-z

Cited by 7 publications

(3 citation statements)

References 51 publications

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“…BCIs are employed to classify and interpret human emotional states based on brain activity patterns [93][94][95][96][97][98]. In this context, Teo and Chia [99] worked on EEG data, preprocessed, and segmented into epochs, which were then used to extract spectral features using the Fast Fourier Transform (FFT) algorithm.…”

Section: Emotion Classificationmentioning

confidence: 99%

A Comprehensive Survey of Brain-Computer Interface Technology in Healthcare: Research Perspectives

Vimal Cruz,

Jamal,

Sethuraman

2024

Preprint

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The Brain-Computer Interface (BCI) technology has emerged as a groundbreaking innovation with profound implications across diverse domains, particularly in healthcare. By establishing a direct communication pathway between the human brain and external devices, BCI systems offer unprecedented opportunities for diagnosis, treatment, and rehabilitation, thereby reshaping the landscape of medical practice. However, despite its immense potential, the widespread adoption of BCI technology in clinical settings faces several challenges. These include the need for robust signal acquisition and processing techniques, ensuring user safety and privacy, addressing ethical considerations, and optimizing user training and adaptation. Overcoming these challenges is crucial to unleashing the complete potential of BCI technology in healthcare and realizing its promise of personalized, patient-centric care. This review work underscores the transformative potential of BCI technology in revolutionizing medical practice. This paper offers a comprehensive analysis of medical-oriented BCI applications by exploring the various uses of BCI technology and its potential to transform patient care.

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Section: Emotion Classificationmentioning

confidence: 99%

A Comprehensive Survey of Brain-Computer Interface Technology in Healthcare: Research Perspectives

Vimal Cruz,

Jamal,

Sethuraman

2024

Preprint

View full text Add to dashboard Cite

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“…Extensive reviews have timely addressed the lack of publicly available databases, the development of universal classifiers, deep learning black box problem, its interpretability, security issues, and EEG localization models, and so forth (Acharya et al, 2018;Andrzejak et al, 2001;Boubchir et al, 2017;Goel & Rathee, 2023;Rasheed et al, 2020). Increasing scientific advancements in optimization methods, signal processing methods, machine learning and transfer learning, and deep learning in seizure and epilepsy classification have been observed (Chavan & Desai, 2023;Luo et al, 2022;Rasheed et al, 2020;Shoeibi et al, 2021;Singh & Malhotra, 2022c;Supriya et al, 2020;Tang et al, 2023).…”

Section: Chb-mit Scalp Eeg Database Collected From Children's Hospitalmentioning

confidence: 99%

A review on software and hardware developments in automatic epilepsy diagnosis using EEG datasets

et al. 2023

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Epilepsy is a common non-communicable, group of neurological disorders affecting more than 50 million individuals worldwide. Different approaches of basic, clinical, and translational research of the human brain have been explored to diagnose, treat, and manage the growing no. of cases of epilepsy. Various hospital information from video, images, signals, forms, and so forth, are retrieved and analysed to develop a consensus for such patients. Electroencephalography (EEG) tests are routinely used to diagnose the type of epilepsy in a clinical setting. Artificial Intelligence algorithms are assisting in the early detection and prediction of epileptic patterns observed in EEG signals. This paper reviews recent and emerging state-of-the-art (SOTA) software and hardware approaches in data selection, signal processing, feature estimation, classification, detection methods, and evaluation metrics applied to open and private EEG datasets from 2014 to 2022. The work summarizes and compares reported works through subjective and objective parameters. Rise in hardware pipeline, start-ups, and companies, deep learning methods in software pipeline and release of free EEG datasets have been observed from 2014 to 2022. SOTA

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“…It is considered more feasible, and cost-effective in terms of real-time BCI implementation (Kiranyaz et al, 2021). Several EEG time series applications like seizure detection (Anuragi et al, 2021(Anuragi et al, , 2022Mehla et al, 2021;Rajendra Acharya et al, 2018;Sharma et al, 2020), epilepsy diagnosis (Abdulhay et al, 2020;Nishad & Pachori, 2020;Serna et al, 2020), subject identification (Rathee et al, 2022;Sun et al, 2019), drowsing states during driving (Doniec et al, 2020), depth-of-anesthesia (Afshar et al, 2021;Altıntop et al, 2022), human activity recognition (Garima, Goel & Rathee, 2023;Lin & Jianning, 2020), and so forth, of 1D-CNN architecture variants have been proposed in order to detect, and treat the abnormal brain activities at an early stage.…”

mentioning

confidence: 99%

One‐dimensional atrous conv‐net based architecture for automatic diagnosis of epilepsy using electroencephalography signals and its brain–computer interface applications

Handa,

Gupta,

Gupta

et al. 2023

Expert Systems

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Precise monitoring and diagnosis of epilepsy by manual analysis of EEG signals are challenging due to the low doctor‐to‐patient ratio, and shortage of medical resources. To automate this diagnosis in real‐time, EEG based Brain–Computer Interface (BCI) system with integration of artificial intelligence techniques will prove to be propitious. This work proposes an end‐to‐end, one‐dimensional atrous conv‐net‐based architecture for automatic epilepsy diagnosis using EEG signals with a conceptual framework of the EEG‐BCI system for routine monitoring and clinical use. The proposed architecture has a robust backbone of six blocks of atrous convolutional layers activated with exponential linear unit functions. The six blocks are followed by the addition of a long short‐term memory layer for automatic feature extraction and sequential EEG data analysis. The efficacy of the proposed architecture has been verified on three publicly available EEG datasets using various evaluation metrics, feature maps, test set evaluation, and ablation studies. An average training and validation accuracy of 96.16% and 90.80% has been achieved upon multiple runs for the three datasets. Ablation experiments indicate that the addition of each block contributed to increasing 17%–25% accuracy scores during the classification of epileptic and non‐epileptic EEG signals. The real‐time EEG‐BCI has been analyzed using weight optimization of the proposed architecture through the NVIDIA Tensor RT framework on a 40 GB DGX A100 NVIDIA workstation. The proposed architecture has generalized well in comparison with the existing techniques for the three EEG datasets and achieved a low training and validation loss with optimum evaluation metrics. This makes the proposed architecture suitable for future EEG‐BCI system deployment in the automatic diagnosis of epilepsy.

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Modified multidimensional scaling on EEG signals for emotion classification

Cited by 7 publications

References 51 publications

A Comprehensive Survey of Brain-Computer Interface Technology in Healthcare: Research Perspectives

A Comprehensive Survey of Brain-Computer Interface Technology in Healthcare: Research Perspectives

A review on software and hardware developments in automatic epilepsy diagnosis using EEG datasets

One‐dimensional atrous conv‐net based architecture for automatic diagnosis of epilepsy using electroencephalography signals and its brain–computer interface applications

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