A Real Time EEG Analysis System

Garza, Jonathan; Li, Yuezhe; Chang, Yuchou; Lin, Hong

doi:10.1109/dsc.2016.29

Cited by 2 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the real-time emotion recognition system, to the best of our knowledge, there are few reports on relevant work. Jonathan et al ( 2016 ) developed a mobile application for processing and analyzing EEG signals, which can display the EEG spectrum, classify EEG signals, visualize, and analyze the classification results. However, this system does not realize the data upload function, and only uses the offline data that has been stored on the server.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

SSTM-IS: simplified STM method based on instance selection for real-time EEG emotion recognition

Ran

Wang

Duan

et al. 2023

Front. Hum. Neurosci.

View full text Add to dashboard Cite

IntroductionEEG signals can non-invasively monitor the brain activities and have been widely used in brain-computer interfaces (BCI). One of the research areas is to recognize emotions objectively through EEG. In fact, the emotion of people changes over time, however, most of the existing affective BCIs process data and recognize emotions offline, and thus cannot be applied to real-time emotion recognition.MethodsIn order to solve this problem, we introduce the instance selection strategy into transfer learning and propose a simplified style transfer mapping algorithm. In the proposed method, the informative instances are firstly selected from the source domain data, and then the update strategy of hyperparameters is also simplified for style transfer mapping, making the model training more quickly and accurately for a new subject.ResultsTo verify the effectiveness of our algorithm, we carry out the experiments on SEED, SEED-IV and the offline dataset collected by ourselves, and achieve the recognition accuracies up to 86.78%, 82.55% and 77.68% in computing time of 7s, 4s and 10s, respectively. Furthermore, we also develop a real-time emotion recognition system which integrates the modules of EEG signal acquisition, data processing, emotion recognition and result visualization.DiscussionBoth the results of offline and online experiments show that the proposed algorithm can accurately recognize emotions in a short time, meeting the needs of real-time emotion recognition applications.

show abstract

Section: Related Workmentioning

confidence: 99%

“…On the real-time emotion recognition system, to the best of our knowledge, there are few reports on relevant work. Jonathan et al (2016) Besides they all use the offline datasets or simulated real-time emotion recognition for verification, not performing the real-time emotion recognition applications in real scene.…”

Section: Related Workmentioning

confidence: 99%