Key result
A vector autoregressive (VAR) model approach effectively classified multichannel EEG signals to discriminate between control and alcoholic subjects, achieving competitive results compared to existing techniques.
Why the study?
Despite being well adapted to modeling temporal correlations and interdependencies in multichannel signals, only a few studies have employed the vector autoregressive model for classification.
A vector autoregressive model provides an effective approach for multichannel signal classification, as demonstrated on EEG data.
VAR models may aid EEG alcoholism classification; hypothesis-generating and requires prospective validation before clinical use.
The analysis of multichannel signals (MCS) has received a great deal of attention in the past few years. Modeling MCS requires depicting not only the temporal correlations within each single-channel signal (SCS) but also the interdependencies between marginal signals. The vector autoregressive (VAR) model is well adapted to providing insights to these ubiquitous dependencies, which is why it has been widely adopted for forecasting and analyzing impulse responses. Despite that, only a few studies have employed the VAR model for classification. To further explore this area, we propose a simple yet effective approach based on modeling MCS with a VAR process. To demonstrate the performance of our approach, we test it on real EEG recordings to discriminate between control and alcoholic subjects. Experimental results show that the proposed VAR approach can be very effective in MCS classification; it achieves competitive results on the benchmark dataset compared to existing state-of-the-art techniques.
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Haboub et al. (2020) studied Alcoholism (EEG classification). Vector autoregressive (VAR) model vs. Existing state-of-the-art techniques was evaluated on Discrimination between control and alcoholic subjects using EEG recordings. A vector autoregressive (VAR) model approach effectively classified multichannel EEG signals to discriminate between control and alcoholic subjects, achieving competitive results compared to existing techniques.
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