{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T09:06:35Z","timestamp":1771232795164,"version":"3.50.1"},"reference-count":34,"publisher":"MIT Press - Journals","issue":"10","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Neural Computation"],"published-print":{"date-parts":[[2019,10]]},"abstract":"<jats:p> In this letter, we propose two novel methods for four-class motor imagery (MI) classification using electroencephalography (EEG). Also, we developed a real-time health 4.0 (H4.0) architecture for brain-controlled internet of things (IoT) enabled environments (BCE), which uses the classified MI task to assist disabled persons in controlling IoT-enabled environments such as lighting and heating, ventilation, and air-conditioning (HVAC). The first method for classification involves a simple and low-complex classification framework using a combination of regularized Riemannian mean (RRM) and linear SVM. Although this method performs better compared to state-of-the-art techniques, it still suffers from a nonnegligible misclassification rate. Hence, to overcome this, the second method offers a persistent decision engine (PDE) for the MI classification, which improves classification accuracy (CA) significantly. The proposed methods are validated using an in-house recorded four-class MI data set (data set I, collected over 14 subjects), and a four-class MI data set 2a of BCI competition IV (data set II, collected over 9 subjects). The proposed RRM architecture obtained average CAs of 74.30% and 67.60% when validated using datasets I and II, respectively. When analyzed along with the proposed PDE classification framework, an average CA of 92.25% on 12 subjects of data set I and 82.54% on 7 subjects of data set II is obtained. The results show that the PDE algorithm is more reliable for the classification of four-class MI and is also feasible for BCE applications. The proposed low-complex BCE architecture is implemented in real time using Raspberry Pi 3 Model B+ along with the Virgo EEG data acquisition system. The hardware implementation results show that the proposed system architecture is well suited for body-wearable devices in the scenario of Health 4.0. We strongly feel that this study can aid in driving the future scope of BCE research. <\/jats:p>","DOI":"10.1162\/neco_a_01223","type":"journal-article","created":{"date-parts":[[2019,8,8]],"date-time":"2019-08-08T18:39:19Z","timestamp":1565289559000},"page":"1915-1944","source":"Crossref","is-referenced-by-count":5,"title":["A Real-Time Health 4.0 Framework with Novel Feature Extraction and Classification for Brain-Controlled IoT-Enabled Environments"],"prefix":"10.1162","volume":"31","author":[{"given":"B.","family":"Jagadish","sequence":"first","affiliation":[{"name":"WiNet Lab, Department of Electrical Engineering, Indian Institute of Technology Hyderabad, Telangana, 502285, India"}]},{"given":"P. K.","family":"Mishra","sequence":"additional","affiliation":[{"name":"WiNet Lab, Department of Electrical Engineering, Indian Institute of Technology Hyderabad, Telangana, 502285, India"}]},{"given":"M. P. R. S.","family":"Kiran","sequence":"additional","affiliation":[{"name":"WiNet Lab, Department of Electrical Engineering, Indian Institute of Technology Hyderabad, Telangana, 502285, India"}]},{"given":"P.","family":"Rajalakshmi","sequence":"additional","affiliation":[{"name":"WiNet Lab, Department of Electrical Engineering, Indian Institute of Technology Hyderabad, Telangana, 502285, India"}]}],"member":"281","reference":[{"key":"B1","author":"Ang K. K.","year":"2008","journal-title":"BCI Competition IV Dataset 2A Results"},{"issue":"2","key":"B2","first-page":"153","volume":"57","author":"Angel J. 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