@article{MAKHILLJEAS201712314125,
    title = {Slow and Fast Eeg Waves Analysis for Kolb’s Learning Style Classification},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {12},
    number = {3},
    pages = {508-512},
    year = {2017},
    issn = {1816-949x},
    doi = {jeasci.2017.508.512},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2017.508.512},
    author = {Nazre,Mohd.,Sahrim,Norizam and},
    keywords = {EEG,learning style,classification,summative,demonstrated},
    abstract = {This research is meant to classify learners based on the combination of Kolb’s learning style
information and Electroencephalogram (EEG) dataset. Slow waves and fast waves EEG of a learners (N = 131)
were captured using the waverider pro hardware and processed using the accompanied software called
waveware to generate the summative EEG as a final dataset. Next, the learners LS were determined using Kolb’s
Learning Style Inventory (KLSI) which clustered them into the LS of diverger assimilator, converger and
Accommodator respectively. The SPSS 16 Modules of 2-steps cluster analysis is used to analyze the summative
EEG dataset of beta and alpha (fast waves); theta and delta (slow waves). As to establish the LS classification
on both waves condition. In term of single EEG band, all LS are correctly clustered (100%) in a homogenous
group notwithstanding fast wave or slow wave EEG. On the other hand, in combined EEG bands, both waves
group had demonstrated a best classification (100%) for LS diverger. Concurrently, best classification (100%)
also obtained for LS accommodator but only in EEG Fast wave condition. Based on the overall findings, the fast
wave EEG is found to be a better classifier for Kolb’s LS compared to the slow wave EEG. The research findings
could be utilized to impart further attention and focus on Beta and Alpha EEG waves in order to infer the
learner’s learning preference based on KLSI.}
    }