  Functional near-infrared spectroscopy (fNIRS) is employed as a non-invasive
method to monitor functional brain activation by capturing changes in the
concentrations of oxygenated haemoglobin (HbO) and deoxygenated haemo-globin
(HbR). Various machine learning classification techniques have been utilized to
distinguish cognitive states. However, conventional machine learning methods,
although simpler to implement, undergo a complex pre-processing phase before
network training and demonstrate reduced accuracy due to inadequate data
preprocessing. Additionally, previous research in cog-nitive load assessment
using fNIRS has predominantly focused on differ-sizeentiating between two
levels of mental workload. These studies mainly aim to classify low and high
levels of cognitive load or distinguish between easy and difficult tasks. To
address these limitations associated with conven-tional methods, this paper
conducts a comprehensive exploration of the im-pact of Long Short-Term Memory
(LSTM) layers on the effectiveness of Convolutional Neural Networks (CNNs)
within deep learning models. This is to address the issues related to spatial
features overfitting and lack of tem-poral dependencies in CNN in the previous
studies. By integrating LSTM layers, the model can capture temporal
dependencies in the fNIRS data, al-lowing for a more comprehensive
understanding of cognitive states. The primary objective is to assess how
incorporating LSTM layers enhances the performance of CNNs. The experimental
results presented in this paper demonstrate that the integration of LSTM layers
with Convolutional layers results in an increase in the accuracy of deep
learning models from 97.40% to 97.92%.
