Implementation of a deep learning model for emotion evaluation based on LSTM psychological and physiological data

Ting, Yen Wei; Zhang, Yun Jie; Chung, Kuo Hsuan; Chang, Yu Fan

doi:10.1109/cacs52606.2021.9639060

Cited by 1 publication

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Currently, many EEG sensors are available, thus allowing BCIs to be investigated extensively. Examples include the operation of computers (Márquez et al, 2018) and web browsing applications (Halder et al, 2015), control of wheeled robots (Alsammarraie & Inan, 2022;Hiraishi, 2015) and robot arms (Ranky & Adamovich, 2010), cognitive state analysis in sports (Hiraishi, 2021) and driving (Hiraishi, 2020), patient monitoring (Kumar et al, 2015), and some medical applications (Saravanarajan et al, 2021;Ting et al, 2021). Thus, many topics related to BCI in diverse areas have been reported.…”

Section: Introductionmentioning

confidence: 99%

“…LSTM is a deep learning method-a type of recurrent neural network-and is advantageous in that it allows the analysis of time-series data such as brainwaves. These methods are typically used for brain data analysis (Costantini et al, 2009;Edla et al, 2018;Liao et al, 2018;Roy et al, 2019;Ting et al, 2021). That is the reason why they were selected in this study.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of Brainwave Sensors and Mental State Classifiers

Hiraishi

2022

International Journal of Artificial Intelligence and Machine Learning

View full text Add to dashboard Cite

Brain-computer interfaces (BCIs) have been attracting attention as a research topic. BCI has various applications, such as at home and in the medical sector. BCI is an interconnection between the human brain and a computer, which is a communication pathway between external peripheral devices. Brainwave sensors play a significant role when applying BCIs in practice. In this study, data from such sensors are analyzed to classify the mental states of users. This study used two different brainwave sensors: Neurosky MindWave Mobile and Emotiv EPOC+. Several types of machine-learning techniques (support vector machine, random forest, and long short-term memory) have been applied to classify brainwave data. This study aimed to compare the accuracy of the two sensors, analyze data, and identify the most accurate machine-learning method. Finally, a BCI toy with MaBeee, which is a battery-type internet-of-things device, was designed as a BCI application that reflected the analysis results.

show abstract