TY - GEN
T1 - Decoding Visual Recognition of Objects from EEG Signals based on Attention-Driven Convolutional Neural Network
AU - Kalafatovich, Jenifer
AU - Lee, Minji
AU - Lee, Seong Whan
N1 - Funding Information:
This work was supported in part by the Institute for Information & Communications Technology Promotion (IITP) grant, funded by the Korea government (MSIT) (No. 2015-0-00185, Development of Intelligent Pattern Recognition Softwares for Ambulatory Brain Computer Interface; No. 2017-0-00451, Development of BCI based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning; No. 2019-0-00079, Artificial Intelligence Graduate School Program (Korea University)).
Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/11
Y1 - 2020/10/11
N2 - The ability to perceive and recognize objects is fundamental for the interaction with the external environment. Studies that investigate them and their relationship with brain activity changes have been increasing due to the possible application in an intuitive brain-machine interface (BMI). In addition, the distinctive patterns when presenting different visual stimuli that make data differentiable enough to be classified have been studied. However, reported classification accuracy still low or employed techniques for obtaining brain signals are impractical to use in real environments. In this study, we aim to decode electroencephalography (EEG) signals depending on the provided visual stimulus. Subjects were presented with 72 photographs belonging to 6 different semantic categories. We classified 6 categories and 72 exemplars according to visual stimuli using EEG signals. In order to achieve a high classification accuracy, we proposed an attention driven convolutional neural network and compared our results with conventional methods used for classifying EEG signals. We reported an accuracy of 50.37 ± 6.56% and 26.75 ± 10.38% for 6-class and 72-class, respectively. These results statistically outperformed other conventional methods. This was possible because of the application of the attention network using human visual pathways. Our findings showed that EEG signals are possible to differentiate when subjects are presented with visual stimulus of different semantic categories and at an exemplar-level with a high classification accuracy; this demonstrates its viability to be applied it in a real-world BMI.
AB - The ability to perceive and recognize objects is fundamental for the interaction with the external environment. Studies that investigate them and their relationship with brain activity changes have been increasing due to the possible application in an intuitive brain-machine interface (BMI). In addition, the distinctive patterns when presenting different visual stimuli that make data differentiable enough to be classified have been studied. However, reported classification accuracy still low or employed techniques for obtaining brain signals are impractical to use in real environments. In this study, we aim to decode electroencephalography (EEG) signals depending on the provided visual stimulus. Subjects were presented with 72 photographs belonging to 6 different semantic categories. We classified 6 categories and 72 exemplars according to visual stimuli using EEG signals. In order to achieve a high classification accuracy, we proposed an attention driven convolutional neural network and compared our results with conventional methods used for classifying EEG signals. We reported an accuracy of 50.37 ± 6.56% and 26.75 ± 10.38% for 6-class and 72-class, respectively. These results statistically outperformed other conventional methods. This was possible because of the application of the attention network using human visual pathways. Our findings showed that EEG signals are possible to differentiate when subjects are presented with visual stimulus of different semantic categories and at an exemplar-level with a high classification accuracy; this demonstrates its viability to be applied it in a real-world BMI.
KW - attention
KW - brain-machine interface
KW - convolutional neural network (CNN)
KW - electroencephalography (EEG)
KW - visual recognition
UR - http://www.scopus.com/inward/record.url?scp=85098891068&partnerID=8YFLogxK
U2 - 10.1109/SMC42975.2020.9283434
DO - 10.1109/SMC42975.2020.9283434
M3 - Conference contribution
AN - SCOPUS:85098891068
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 2985
EP - 2990
BT - 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
Y2 - 11 October 2020 through 14 October 2020
ER -