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The widely adopted paradigm in brain–computer interfaces (BCIs) involves motor imagery (MI), enabling improved communication between humans and machines. EEG signals derived from MI present several challenges due to their inherent characteristics, which lead to a complex process of classifying and finding the potential tasks of a specific participant. Another issue is that BCI systems can result in noisy data and redundant channels, which in turn can lead to increased equipment and computational costs. To address these problems, the optimal channel selection of a multiclass MI classification based on a Fusion convolutional neural network with Attention blocks (FCNNA) is proposed. In this study, we developed a CNN model consisting of layers of convolutional blocks with multiple spatial and temporal filters. These filters are designed specifically to capture the distribution and relationships of signal features across different electrode locations, as well as to analyze the evolution of these features over time. Following these layers, a Convolutional Block Attention Module (CBAM) is used to, further, enhance EEG signal feature extraction. In the process of channel selection, the genetic algorithm is used to select the optimal set of channels using a new technique to deliver fixed as well as variable channels for all participants. The proposed methodology is validated showing 6.41% improvement in multiclass classification compared to most baseline models. Notably, we achieved the highest results of 93.09% for binary classes involving left-hand and right-hand movements. In addition, the cross-subject strategy for multiclass classification yielded an impressive accuracy of 68.87%. Following channel selection, multiclass classification accuracy was enhanced, reaching 84.53%. Overall, our experiments illustrated the efficiency of the proposed EEG MI model in both channel selection and classification, showing superior results with either a full channel set or a reduced number of channels.

The keystroke-level model (KLM) is the simplest model of the goals, operators, methods, and selection rules (GOMS) family. The KLM computes formative quantitative predictions of task execution time. This paper provides a systematic literature review of KLM extensions across various applications and setups. The objective of this review is to address research questions concerning the development and validation of extensions. A total of 54 KLM extensions have been exhaustively reviewed. The results show that the original keystroke and mental act operators were continuously preserved or adapted and that the drawing operator was used the least. Excluding the original operators, almost 45 operators were collated from the primary studies. Only half of the studies validated their model’s efficiency through experiments. The results also identify several research gaps, such as the shortage of KLM extensions for post-GUI/WIMP interfaces. Based on the results obtained in this work, this review finally provides guidelines for researchers and practitioners.