Electroencephalography (EEG) is widely utilized in neuroscience and clinical applications, serving as a noninvasive method for monitoring brain activity. However, despite its extensive use, the multitude of channels recorded by scalp electrodes presents challenges, including impractical usage, heightened model complexity, and potential overfitting issues during training. This paper addresses the challenges of high dimensionality in EEG data and introduces two innovative EEG channel selection algorithms, achieving significant reductions in channels, model size, and complexity while maintaining high classification accuracy. Validated through experiments on EEGNet and the MNE/BCI Competition IV 2a datasets, these algorithms prove valuable for practical and cost-efficient scenarios. LSvT-S emphasizes high channel reduction and low complexity, while LSvT-G targets faster channel selection, offering users choices. Experiments on MNE and BCI Competition IV 2a datasets show that LSvT-S achieves a remarkable 96.7% and 81.8% reduction in channels, along with 44% and 12.4% reductions in model size, and 96.2% and 80.8% in computation complexity, respectively. Meanwhile, LSvT-G achieves an additional speedup ranging from 32.8x to 7.6x.

Recently, many applications increasingly demand content-based image retrieval. To speed up the search process, a signature file containing the signatures associated with database images is frequently used as a filter to prune off non-promising images at the early stage of query processing. We generate the signature for each picture with respect to "key" objects and propose a novel structure for organizing signatures. By using this new indexing structure, the number of signatures to be examined per query is reduced significantly. In particular, the number of bits to be examined per query is fewer as compared to the signature tree method proposed by Y. Chen (see Processing Letters, vol.82, p.213-21, 2002)

Spatial relationships between objects are important features for designing a content-based image retrieval system. We propose a new scheme, called 9D-SPA representation, for encoding the spatial relations in an image. With this representation, important functions of intelligent image database systems such as visualization, browsing, spatial reasoning, iconic indexing, and similarity retrieval can be easily achieved. The capability of discriminating images based on 9D-SPA representation is much more powerful than any spatial representation method based on minimum bounding rectangles or centroids of objects. The similarity measures using 9D-SPA representation provide a wide range of fuzzy matching capability in similarity retrieval to meet different user's requirements. Experimental results showed that our system is very effective in terms of recall and precision. In addition, the 9D-SPA representation can be incorporated into a two-level index structure to help reduce the search space of each query processing. The experimental results also demonstrated that, on average, only 0.1254 percent /spl sim/ 1.6829 percent of symbolic pictures (depending on various degrees of similarity) were accessed per query in an image database containing 50,000 symbolic pictures.