Brain-Computer Interface (BCI) technology facilitates direct communication between the human brain and external devices, offering novel avenues for interaction and rehabilitation. Among BCIs, Electroencephalogram (EEG...
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ISBN:
(数字)9798350368284
ISBN:
(纸本)9798350368291
Brain-Computer Interface (BCI) technology facilitates direct communication between the human brain and external devices, offering novel avenues for interaction and rehabilitation. Among BCIs, Electroencephalogram (EEG)-based motor imagery (MI) is a key technique, allowing cognitive signals to be transformed into executable commands. Nevertheless, challenges in accurately decoding brain signals limit the broader application of this technology. In this study, we propose the CAT-CNN-TRFM network, designed for EEG-based MI classification. The network employs a channel attention mechanism to selectively enhance relevant EEG channels. A convolutional module is subsequently used to extract spatiotemporal features, followed by a Transformer encoder to model global dependencies through multi-head attention. Finally, a fully connected layer handles the classification of signals. On the BCI IV2a dataset, CAT-CNN-TRFM achieves an average accuracy of 81.25%. Ablation studies and hyperparameter analysis confirm the essential contribution of the channel attention mechanism, Transformer module, and data augmentation strategy in optimizing the model's performance. These results highlight the promise of CAT-CNN-TRFM for addressing complex EEG signal classification challenges.
This paper presents a means of automatically deriving a hierarchical organization of concepts from a set of documents without use of training data or standard clustering techniques. Instead, salient words and phrases ...
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ISBN:
(纸本)1581130961
This paper presents a means of automatically deriving a hierarchical organization of concepts from a set of documents without use of training data or standard clustering techniques. Instead, salient words and phrases extracted from the documents are organized hierarchically using a type of co-occurrence known as subsumption. The resulting structure is displayed as a series of hierarchical menus. When generated from a set of retrieved documents, a user browsing the menus is provided with a detailed overview of their content in a manner distinct from existing overview and summarization techniques. The methods used to build the structure are simple, but appear to be effective: a small-scale user study reveals that the generated hierarchy possesses properties expected of such a structure in that general terms are placed at the top levels leading to related and more specific terms below. The formation and presentation of the hierarchy is described along with the user study and some other informal evaluations.
This is a time of increasing interdisciplinary research. Computer science is learning more from biology every day, enabling a plethora of new software techniques to flourish. And biology is now beginning to see the re...
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During the past decades some very interesting results have been obtained in controller synthesis using Linear Parameter-Varying (LPV) systems. However, the LPV models are commonly required to be transformed into State...
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