Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers

By J. Atkinson, D. Campos. Expert systems with applications, 2016

370 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/f7ce678a8b5edc590f176b13bfbb5474237adfbc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Reda A. El-Khoribi
2. Salma Alhagry
3. A. Hammad
4. Essam H. Houssein
5. Weifeng Ge
6. Aly Aly Fahmy
7. Abdelmgeid A. Ali

In Experimental and Cognitive Psychology

1. Reda A. El-Khoribi
2. Salma Alhagry
3. A. Hammad
4. Essam H. Houssein
5. Aly Aly Fahmy
6. Abdelmgeid A. Ali

Citations in the corpus

• A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, methodology): “A ffectiv e C o m p u tin g U n im o d al A ffect R eco g n itio n T ex t S e n tim e n t A n a ly sis A u d io E m o tio n R e co g n itio n P h y sio lo g ica l-b a se d E m o tio n R ec o g n itio n Multi-physical Modalities Fusion Multi- physiological modalities fusion V isu a l E m o tio n R e ”
• Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review (2022, methodology): “To monitor temporal emotional fluctuations in real time, traditional time series analysis approaches must be integrated with machine learning techniques [55, 199–203].”
• Emotion Recognition based on EEG using LSTM Recurrent Neural Network (2017, methodology): “[2] Atkinson and Campos [3] Yoon and Chung [6] Naser and Saha [7] proposed method”