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Emotion recognition provides valuable information for human\u2013computer interactions; however, the large number of input channels (&gt;\u2009200) and modalities (&gt;\u20093 ) involved in emotion recognition are significantly expensive from a memory perspective. To address this, methods for memory reduction and optimization are proposed, including a novel approach that takes advantage of the combinatorial nature of the encoding process, and an elementary cellular automaton. HDC with early sensor fusion is implemented alongside the proposed techniques achieving two-class multi-modal classification accuracies of &gt;\u200976% for valence and &gt;\u200973% for arousal on the multi-modal AMIGOS and DEAP data sets, almost always better than state of the art. The required vector storage is seamlessly reduced by 98% and the frequency of vector requests by at least 1\/5. The results demonstrate the potential of efficient hyperdimensional computing for low-power, multi-channeled emotion recognition tasks.<\/jats:p>","DOI":"10.1186\/s40708-022-00162-8","type":"journal-article","created":{"date-parts":[[2022,6,27]],"date-time":"2022-06-27T15:02:45Z","timestamp":1656342165000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Efficient emotion recognition using hyperdimensional computing with combinatorial channel encoding and cellular automata"],"prefix":"10.1186","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8483-1810","authenticated-orcid":false,"given":"Alisha","family":"Menon","sequence":"first","affiliation":[]},{"given":"Anirudh","family":"Natarajan","sequence":"additional","affiliation":[]},{"given":"Reva","family":"Agashe","sequence":"additional","affiliation":[]},{"given":"Daniel","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Melvin","family":"Aristio","sequence":"additional","affiliation":[]},{"given":"Harrison","family":"Liew","sequence":"additional","affiliation":[]},{"given":"Yakun Sophia","family":"Shao","sequence":"additional","affiliation":[]},{"given":"Jan M.","family":"Rabaey","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,27]]},"reference":[{"key":"162_CR1","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.cmpb.2016.12.005","volume":"140","author":"Z Yin","year":"2017","unstructured":"Yin Z, Zhao M, Wang Y, Yang J, Zhang J (2017) Recognition of emotions using multimodal physiological signals and an ensemble deep learning model. 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