Analysis of the Information Transfer Rate-ITR in Linear and Non-linear Feature Extraction Methods for SSVEP Signals
The most popular paradigm in BCIs is the steady-state visually evoked potential (SSVEP) due to their advantages, such as the high information transfer rate (ITR), the time spent on users in the training phase, and the capacity to discriminate each stimulus. One of the most influential factors in the ITR evaluation is the feature extraction methods since these can increase the accuracy. Here, we compare nine methods for the extraction of features from SSVEP signals to identify those with better performance, according to the time window (TW), its technology (equipment and number of nodes), and the value of ITR. The study identifies two groups: the first one is characterized by presenting variations of correlated component analysis (CCA), which is highly used to increase the ITR due to its efficiency in classification and its capacity of response to reduction (TW), such as MsetCCA, IT-CCA, FBCCA; the second one are the representation special based methods that consider the non-linear nature of the electroencephalogram (EEG) signal such as TRCA, CORRCA, EMD, and VMD. The results show a considerable difference between these groups. The maximum ITR value for FBCCA was 117.75 [bits/min] in a TW of 1.25s, while the VMD method achieved 3120 [bits/min] in a TW of 1s, respectively. The comparison covers signals between 0.55 and 8 seconds, taking into account visual strain, the experimental environment, and other artifacts.
Ujwal Chaudhary, Niels Birbaumer, and Ander Ramos-Murguialday. Brain-computer interfaces for communication and rehabilitation. Nature Reviews Neurology, vol. 12, no. 19, pp. 513-525, Sep 2016. DOI: 10.1038/nrneurol.2016.113.
Vishwakarma, R et al., "EEG Signals Analysis And Classification For BCI Systems: A Review." IC-ETITE, pp. 1-6. Feb. 2020. DOI: 10.1109/ic-ETITE47903.2020.066.
Seyed Mohammad Mehdi Sa, Mohammad Pooyan, and Ali Motie Nasrabadi. "Improving the performance of the SSVEP-based BCI system using optimized singular spectrum analysis OSSA." Biomed. Signal Process. Control., vol. 46. pp. 46-58, July 2018. DOI: https://doi.org/10.1016/j.bspc.2018.06.010.
Daiki Aminaka, S. Makino, and Tomasz M. Rutkowski. "Chromatic SSVEP BCI paradigm targeting the higher frequency EEG responses." APSIPA, pp. 1-7, Dec. 2014. DOI: 10.1109/APSIPA.2014.7041761
Yangsong Zhang, Peng Xu, Kaiwen Cheng, and Dezhong Yao. "Multivariate synchronization index for frequency recognition of SSVEP-based brain-computer interface." Journal of Neuroscience Methods, vol 221, pp. 32-40, Jan 2014. DOI: https://doi.org/10.1016/j.jneumeth.2013.07.018.
Jesus Minguillon, M. A. Lopez-Gordo, and F. Pelayo. "Trends in EEG-BCI for daily-life: Requirements for artifact removal." Biomed Signal Process Control., vol 31, pp. 407-418, Jan 2017. DOI: https://doi.org/10.1016/j.bspc.2016.09.005.
S. Gao, Yijun Wang, X. Gao, and Bo Hong. "Visual and Auditory Brain-Computer Interfaces." IEEE Transactions on Biomedical Engineering, vol. 61, no. 5, pp. 1436-1447, May. 2014. DOI: DOI: 10.1109/TBME.2014.2300164.
Zafer İşcan, Vadim V. Nikulin, Xu Lei, "Steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) performance under different perturbations." PLOS ONE, vol. 13, no. 1, pp. e0191673, Jan 2018. DOI: https://doi.org/10.1371/journal.pone.0191673.
Qingguo Wei, M. Xiao, and Zongwu Lu. "A Comparative Study of Canonical Correlation Analysis and Power Spectral Density Analysis for SSVEP Detection." 3rd Int. Conf. on Intell. Human-Machine Sys. and Cybernetics, vol 2, pp. 7-10, Oct. 2011. DOI 10.1109/IHMSC.2011.72.
X. Chen, Yijun Wang, S. Gao, T. Jung, and X. Gao. "Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface." Journal of neural engineering, vol. 12, no. 4, pp. 046008, 2015. DOI: 10.1088/1741-2560/12/4/046008.
Yijun Wang, X. Chen, X. Gao, and S. Gao. "A Benchmark Dataset for SSVEP-Based Brain-Computer Interfaces." IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 10 pp. 1746-1752, 2017. DOI: 10.1109/TNSRE.2016.2627556.
Sandra Ebele Nwachukwu, Minghui Shi, Chang Liu, X. Liu, Changle Zhou, F. Chao, M. Jiang, Penglin Kang, and Zilong Li. "An SSVEP Recognition Method by Combining Individual Template with CCA." In ICIAI 2019, pp. 6-10, March 2019. DOI: 10.1145/3319921.3319925.
Y. Zhang, G. Zhou, J. Jin, X. Wang, and A. Cichocki. "Frequency Recognition in SSVEP-Based BCI using Multiset Canonical Correlation Analysis." International journal of neural systems, vol. 24, no. 4, pp. 1450013, Jun. 2014. DOI: 10.1142/S0129065714500130.
Y. Jiao, Y. Zhang, Yu Wang, B. Wang, J. Jin, and X. Wang. "A Novel Multilayer Correlation Maximization Model for Improving CCA-Based Frequency Recognition in SSVEP Brain-Computer Interface." Int. Jou. of neural systems, vol. 28, no. 4, pp. 1750039, May 2018. DOI: 10.1142/S0129065717500393.
H. Tanaka, T. Katura, and H. Sato. "Task-related component analysis for functional neuroimaging and application to near-infrared spectroscopy data." NeuroImage, vol 64, pp. 308-327, 2013. DOI: https://doi.org/10.1016/j.neuroimage.2012.08.044.
Yangsong Zhang, Daqing Guo, Fali Li, E. Yin, Y. Zhang, Peiyang Li, Q. Zhao, T. Tanaka, Dezhong Yao, and Peng Xu. "Correlated Component Analysis for Enhancing the Performance of SSVEP-Based Brain-Computer Interface." IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol 26, no. 5, pp. 948-956, May. 2018. DOI: 10.1109/TNSRE.2018.2826541.
Chu, Tai-Yi & Huang, Wen-Cheng. (2020). "Application of Empirical Mode Decomposition Method to Synthesize Flow Data: A Case Study of Hushan Reservoir in Taiwan." Water. Vol. 12, no. 4, pp. 927. Mar 2020 DOI: 10.3390/w12040927.
Gupta, Akshansh & Kumar, Dhirendra & Chakraborti, Anirban & Sharma, Kiran. (2017). Performance Evaluation of Empirical Mode Decomposition Algorithms for Mental Task Classification. 10.1101/076646.
Mahsa Behroozi and M. Daliri. "A high-performance steady state visual evoked potential BCI system based on variational mode decomposition." Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT), pp. 1-4, June 2018. DOI: 10.1109/EBBT.2018.8391421.
Wang, Haoran; Sun, Yaoru; Li, Yunxia; Chen, Shiyi; Zhou, Wei. "Inter- and Intra-subject Template-Based Multivariate Synchronization Index Using an Adaptive Threshold for SSVEP-Based BCIs." Frontiers in Neuroscience, vol. 14, pp. 717, Sep. 2020. DOI: 10.3389/fnins.2020.00717.
Yu Zhang, Jing Jin, Xiangyun Qing, Bei Wang, and Xingyu Wang. "LASSO based stimulus frequency recognition model for SSVEP BCIs." Biomedical Signal Processing and Control, vol. 7, pp. 104-111, Mar 2012. DOI: https://doi.org/10.1016/j.bspc.2011.02.002.
Masaki Nakanishi, Yijun Wang, Yu-Te Wang, and Tzyy-Ping Jung. "A Comparison Study of Canonical Correlation Analysis Based Methods for Detecting Steady-State Visual Evoked Potentials." PLOS ONE, vol. 10, no. 10, pp. e0140703, Oct 2015. DOI: https://doi.org/10.1371/journal.pone.0140703.
Maciej Labecki, Rafal Kus, Alicja Brzozowska, Tadeusz Stacewicz, Basabdatta S. Bhattacharya, and Piotr Suczynski. "Nonlinear Origin of SSVEP Spectra-A Combined Experimental and Modeling Study." Frontiers in Computational Neuroscience, vol. 10, Dec 2016. DOI: https://doi.org/10.3389/fncom.2016.00129.
Song, Wanjiao; Dong, Qing; Xue, Cunjin; Sha, Jin. "Two types of El Niño and extratropical sea-level pressure variations." International Journal of Remote Sensing, vol. 37, no. 22, pp. 5443–5456, Oct. 2016. DOI: 10.1080/01431161.2016.1232872.
- There are currently no refbacks.
Published by INSIGHT - Indonesian Society for Knowledge and Human Development