Application of Stock Trading-Related Emotion Recognition from EEG Signals using Deep Learning EEGNet DOI
Mingliang Zuo, Fei Wang

Published: Nov. 17, 2023

This paper applies deep learning EEGNet to stock emotion recognition using EEG signals, achieving significantly higher accuracy than prior machine methods by utilizing comprehensive feature extraction and selection techniques. In the domain of recognition, previous studies have predominantly relied on classification methods, rooted in Valence/Arousal model electroencephalogram (EEG) signals. distinguishes itself placing a primary focus application techniques, specifically highlighting EEGNet, well-recognized method EEG-related research. The principal objective this research is address issue low within dataset. article offers explanation workflow methodologies employed system, provides detailed descriptions analyses dataset includes five frequency bands various features, including DE, DASM, RASM. Feature utilizes mutual information-based filtering, chi-square statistics, embedded algorithms classifiers. achieves high rates, 95.18% for Arousal 97.9% Valence. stands contrast researchers' ANN which were 70% 71% Valence context datasets. These results underscore its exceptional performance.

Language: Английский

State-of-the-Art on Brain-Computer Interface Technology DOI Creative Commons
Jānis Pekša, Dmytro Mamchur

Sensors, Journal Year: 2023, Volume and Issue: 23(13), P. 6001 - 6001

Published: June 28, 2023

This paper provides a comprehensive overview of the state-of-the-art in brain–computer interfaces (BCI). It begins by providing an introduction to BCIs, describing their main operation principles and most widely used platforms. The then examines various components BCI system, such as hardware, software, signal processing algorithms. Finally, it looks at current trends research related use for medical, educational, other purposes, well potential future applications this technology. concludes highlighting some key challenges that still need be addressed before widespread adoption can occur. By presenting up-to-date assessment technology, will provide valuable insight into where field is heading terms progress innovation.

Language: Английский

Citations

58
The safety and efficacy of applying a high-current temporal interference electrical stimulation in humans DOI Creative Commons
Yan Wang,

Ginger Qinghong Zeng,

Mengmeng Wang

et al.

Frontiers in Human Neuroscience, Journal Year: 2024, Volume and Issue: 18

Published: Nov. 29, 2024

Temporal interference electrical stimulation (TI) is promise in targeting deep brain regions focally. However, limited electric field intensity challenges its efficacy. This study aimed to introduce a high-current TI protocol enhance and evaluate safety efficacy when applied the primary motor cortex (M1) human brain. Safety assessments included battery of biochemical neuropsychological tests (NSE, MoCA, PPT, VAMS-R, SAS measurements), 5-min resting-state electroencephalography (EEG) recordings before after 30-min sessions (20 Hz, 70 sham). Adverse reactions were also documented post-stimulation. Efficacy evaluations involved two tasks, simple reaction time (SRT) task one-increment task, investigate distinct contributions beta Hz) gamma (70 oscillations functions. Biochemical revealed no significant differences between groups. Additionally, epileptic activities detected EEG recordings. In 20 Hz delayed participants' compared sham Conversely, SRT exhibited tendency performance relative group. The proposed both safe effective for stimulating Moreover, effects observed tasks underscore dissociative roles functions, offering valuable insights into potential applications research.

Language: Английский

Citations

2
Towards Implementation of Emotional Intelligence in Human–Machine Collaborative Systems DOI Open Access
Miroslav Markov,

Yasen Kalinin,

Valentina Markova

et al.

Electronics, Journal Year: 2023, Volume and Issue: 12(18), P. 3852 - 3852

Published: Sept. 12, 2023

Social awareness and relationship management components can be seen as a form of emotional intelligence. In the present work, we propose task-related adaptation on machine side that accounts for person’s momentous cognitive state. We validate practical significance proposed approach in person-specific person-independent setups. The analysis results setup shows individual optimal performance curves person, according to Yerkes–Dodson law, are displaced. Awareness these allows automated recognition specific user profiles, real-time monitoring condition, activating particular strategy. This is especially important when deviation detected caused by change state mind under influence known or unknown factors.

Language: Английский

Citations

2
Evaluation of an English language phoneme-based imagined speech brain computer interface with low-cost electroencephalography DOI Creative Commons
John LaRocco,

Qudsia Tahmina,

Sam Lecian

et al.

Frontiers in Neuroinformatics, Journal Year: 2023, Volume and Issue: 17

Published: Dec. 18, 2023

Introduction Paralyzed and physically impaired patients face communication difficulties, even when they are mentally coherent aware. Electroencephalographic (EEG) brain–computer interfaces (BCIs) offer a potential method for these people without invasive surgery or physical device controls. Methods Although virtual keyboard protocols well documented in EEG BCI paradigms, implementations visually taxing fatiguing. All English words combine 44 unique phonemes, each corresponding to pattern. In this study, complete phoneme-based imagined speech was developed tested on 16 subjects. Results Using open-source hardware software, machine learning models, such as k-nearest neighbor (KNN), reliably achieved mean accuracy of 97 ± 0.001%, F1 0.55 0.01, AUC-ROC 0.68 0.002 modified one-versus-rest configuration, resulting an information transfer rate 304.15 bits per minute. line with prior literature, the distinguishing feature between phonemes gamma power channels F3 F7. Discussion However, adjustments selection, trial window length, classifier algorithms may improve performance. summary, iterative changes viable directly deployable current, commercially available systems software. The development intuitive software demonstrates ease which technology could be deployed real-world applications.

Language: Английский

Citations

2
A Hybrid BCI for Robotic Device Navigation DOI
Yih‐Choung Yu,

Hayden Fisher,

Angela Busheska

et al.

Published: March 13, 2024

Applications of brain-computer interface (BCI) systems have grown in importance for assisting individuals with severe motor disabilities navigating our increasingly technologically dependent society. With applications such as electric wheelchairs and advanced prosthetics mind, the goal this research is to develop a system that enables use electroencephalographic (EEG) electromyographic (EMG) signals control movement robot. An EEG cap was used obtain occipital alpha power density, frontal muscular artifacts, sensorimotor mu rhythms, which were then sent back PC via Bluetooth further processing. Signal-processing algorithms models developed implemented determine user's mental activity send external physical device. The preliminary results from pilot experiments very promising. will be real-time signal processing tested BCI-controlled robotic

Language: Английский

Citations

0
Designing a Wearable EEG Device and Its Benefits for Epilepsy Patients: A Review DOI Open Access
Ola Marwan Assim,

Ahlam Fhathl Mahmood

Al-Kitab Journal for Pure Sciences, Journal Year: 2023, Volume and Issue: 7(1), P. 69 - 82

Published: Aug. 20, 2023

Epilepsy is a neurological disorder that causes repeated seizures in millions of people worldwide. Traditional Electroencephalography (EEG) systems can be cumbersome and limited to clinical settings, but they have helped diagnose monitor epilepsy. Wearable EEG devices transformed epilepsy management by providing real-time, non-invasive, continuous monitoring capabilities. This review paper investigates the design considerations technological advancements wearable devices, emphasizing their numerous benefits treating epileptic patients limitation designing devices. In conclusion, integration multimodal data offer comprehensive overview patient's health, enabling implementation personalized efficient treatment approaches.

Language: Английский

Citations

1
Application of Stock Trading-Related Emotion Recognition from EEG Signals using Deep Learning EEGNet DOI
Mingliang Zuo, Fei Wang

Published: Nov. 17, 2023

This paper applies deep learning EEGNet to stock emotion recognition using EEG signals, achieving significantly higher accuracy than prior machine methods by utilizing comprehensive feature extraction and selection techniques. In the domain of recognition, previous studies have predominantly relied on classification methods, rooted in Valence/Arousal model electroencephalogram (EEG) signals. distinguishes itself placing a primary focus application techniques, specifically highlighting EEGNet, well-recognized method EEG-related research. The principal objective this research is address issue low within dataset. article offers explanation workflow methodologies employed system, provides detailed descriptions analyses dataset includes five frequency bands various features, including DE, DASM, RASM. Feature utilizes mutual information-based filtering, chi-square statistics, embedded algorithms classifiers. achieves high rates, 95.18% for Arousal 97.9% Valence. stands contrast researchers' ANN which were 70% 71% Valence context datasets. These results underscore its exceptional performance.

Language: Английский

Citations

0