  The human brain remains continuously active, whether an individual is working
or at rest. Mental activity is a daily process, and if the brain becomes
excessively active, known as overload, it can adversely affect human health.
Recently, advancements in early prediction of mental health conditions have
emerged, aiming to prevent serious consequences and enhance the overall quality
of life. Consequently, the estimation of mental status has garnered significant
attention from diverse researchers due to its potential benefits. While various
signals are employed to assess mental state, the electroencephalogram,
containing extensive information about the brain, is widely utilized by
researchers. In this paper, we categorize mental workload into three states
(low, middle, and high) and estimate a continuum of mental workload levels. Our
method leverages information from multiple spatial dimensions to achieve
optimal results in mental estimation. For the time domain approach, we employ
Temporal Convolutional Networks. In the frequency domain, we introduce a novel
architecture based on combining residual blocks, termed the Multi-Dimensional
Residual Block. The integration of these two domains yields significant results
compared to individual estimates in each domain. Our approach achieved a 74.98%
accuracy in the three-class classification, surpassing the provided data
results at 69.00%. Specially, our method demonstrates efficacy in estimating
continuous levels, evidenced by a corresponding Concordance Correlation
Coefficient (CCC) result of 0.629. The combination of time and frequency domain
analysis in our approach highlights the exciting potential to improve
healthcare applications in the future.
