Data mining based approach to study the effect of consumption of caffeinated coffee on the generation of the steady-state visual evoked potential signals. (December 2019)

• Record Type: Journal Article
• Authors: Tarafdar, Kishore K.; Pradhan, Bikash K.; Nayak, Suraj K.; Khasnobish, Anwesha; Chakravarty, Sumit; Ray, Sirsendu S.; Pal, Kunal
• Is Part Of: Computers in biology and medicine. Volume 115(2019)
• Publication Date: 2019-12
• Subjects: SSVEP -- EEG -- Caffeine -- Canonical correlation analysis -- Recurrence quantification analysis -- Multilayer perceptron network; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2019.103526
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: The steady-state visual evoked potentials (SSVEP), are elicited at the parieto-occipital region of the cortex when a light source (3.5–75 Hz), flickering at a constant frequency, stimulates the retinal cells. In the last few decades, researchers have reported that caffeine enhances the vigilance and the executive control of visual attention. However, no study has investigated the effect of caffeinated coffee on the SSVEP response, which is used for controlling the brain-computer interface (BCI) devices for rehabilitative applications. The current work proposes a data mining-based approach to gain insight into the alterations in the SSVEP signals after the consumption of caffeinated coffee. Recurrence quantification analysis (RQA) of the electroencephalogram (EEG) signals was employed for this purpose. The EEG signals were acquired at seven frequencies of photic stimuli. The stimuli frequencies were chosen such that they were distributed throughout the EEG frequency bands. The prominent SSVEP signals were identified using the Canonical Correlation Analysis (CCA) method. Several statistical features were extracted from the recurrence plot of the SSVEP signals. Statistical analyses using the t -test and decision tree-based methods helped to select the most relevant features, which were then classified using Automated Neural Network (ANN). The relevant features could be classified with a maximum accuracy of 97%. This supports our hypothesis that the consumption of caffeinated coffee can alter the SSVEP response. In conclusion, utmost care should be taken in selecting the features for designing BCI devices. Highlights: The effect of caffeinated coffee on SSVEP signals was studied. Activated regions of the cortex were identified using canonical correlation analysis. Significant RQA features were identified and subsequently classified. SSVEP signal generation was not hampered in the parieto-occipital region. Interestingly, frontal lobe activity of the brain was significantly increased.