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However, the need for long-term monitoring is incompatible with data-scarce settings, and methods trained on one subject often fail on another due to cross-subject variability. To address these limitations, this study proposes a cross-subject, single-channel electroencephalography (EEG)-based method that uses Multi-Entropy Feature Concatenation (MEFC) to classify AD and FTD. First, single-channel EEG is processed through the Discrete Wavelet Transform (DWT) to extract five rhythms: delta, theta, alpha, beta, and gamma. Subsequently, Permutation Entropy (PE), Singular Spectrum Entropy (SSE), and Sample Entropy (SE) are calculated for each rhythm and concatenated to form a combined MEFC to characterize the non-linear dynamic properties of EEG. Lastly, Dynamic Time Warping (DTW), Pearson Correlation Coefficient (PCC), Wavelet Coherence (WC), and Hilbert Transform Correlation (HTC) are employed to measure the similarity between unknown rhythmic MEFC and those from AD, FTD, and Healthy Control (HC) groups, performing a data-driven classification via similarity measurement. Experimental results on 88 subjects in the AHEPA dataset demonstrate that the beta-rhythm with PCC yields a three-class accuracy of 76.14% using single-channel FP2. In another dataset, the Florida-Based dataset, involving 48 subjects, theta-rhythm with WC achieves a two-class accuracy of 83.33% using FP2. Furthermore, a MATLAB R2023b-based toolbox is developed using the proposed method. Such outcomes are impressive, given the limited data per individual (data-efficient), reliable performance across new subjects (cross-subject), and compatibility with wearable devices (single-channel), providing a novel entropy-based approach for EEG-based applications in biomedical engineering.<\/jats:p>","DOI":"10.3390\/e28020212","type":"journal-article","created":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T09:48:34Z","timestamp":1770889714000},"page":"212","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Multi-Entropy Feature Concatenation for Data-Efficient Cross-Subject Classification of Alzheimer\u2019s Disease and Frontotemporal Dementia from Single-Channel EEG"],"prefix":"10.3390","volume":"28","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-8586-9535","authenticated-orcid":false,"given":"Jiawen","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China"},{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China"},{"name":"ZUMRI-LYG Joint Laboratory, Zhuhai UM Science and Technology Research Institute, Zhuhai 519031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0008-2344-9691","authenticated-orcid":false,"given":"Chen","family":"Ling","sequence":"additional","affiliation":[{"name":"School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0006-6668-9420","authenticated-orcid":false,"given":"Weidong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0001-7294-4172","authenticated-orcid":false,"given":"Jujian","family":"Lv","sequence":"additional","affiliation":[{"name":"School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0001-9308-3551","authenticated-orcid":false,"given":"Xianglei","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaihan","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Neijiang Normal University, Neijiang 641004, China"},{"name":"School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610056, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rongjun","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China"},{"name":"Guangdong Provincial Key Laboratory of Intellectual Property and Big Data, Guangdong Polytechnic Normal University, Guangzhou 510665, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Komarova, Y., Zakharov, A., Sergeeva, M., Romanchuk, N., Vladimirova, T., and Shirolapov, I. 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