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Tadashi Yamamoto and Toyohiro Hamaguchi
In this study, we aimed to evaluate the effectiveness of a brain robot in rehabilitation that combines motor imagery (MI), robotic motor assistance, and electrical stimulation. Thirteen in-patients with severe post-stroke hemiplegia underwent electroence...
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Alexey Kozin, Anton Gerasimov, Maxim Bakaev, Anton Pashkov and Olga Razumnikova
Brain?computer interfaces (BCIs) based on steady-state visually evoked potentials (SSVEPs) are inexpensive and do not require user training. However, the highly personalized reaction to visual stimulation is an obstacle to the wider application of this t...
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Xuebin Xu, Chen Chen, Kan Meng, Longbin Lu, Xiaorui Cheng and Haichao Fan
Sleep, as the basis for regular body functioning, can affect human health. Poor sleep conditions can lead to various physical ailments, such as poor immunity, memory loss, slow cognitive development, and cardiovascular diseases. Along the increasing stre...
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Foteini Gramouseni, Katerina D. Tzimourta, Pantelis Angelidis, Nikolaos Giannakeas and Markos G. Tsipouras
The objective of this systematic review centers on cognitive assessment based on electroencephalography (EEG) analysis in Virtual Reality (VR), Augmented Reality (AR) and Mixed Reality (MR) environments, projected on Head Mounted Displays (HMD), in healt...
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Eike Jakubowitz, Thekla Feist, Alina Obermeier, Carina Gempfer, Christof Hurschler, Henning Windhagen and Max-Heinrich Laves
Human grasping is a relatively fast process and control signals for upper limb prosthetics cannot be generated and processed in a sufficiently timely manner. The aim of this study was to examine whether discriminating between different grasping movements...
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Rito Clifford Maswanganyi, Chungling Tu, Pius Adewale Owolawi and Shengzhi Du
Transfer learning (TL) has been proven to be one of the most significant techniques for cross-subject classification in electroencephalogram (EEG)-based brain-computer interfaces (BCI). Hence, it is widely used to address the challenges of cross-session ...
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Çaglar Uyulan, David Mayor, Tony Steffert, Tim Watson and Duncan Banks
The field of signal processing using machine and deep learning algorithms has undergone significant growth in the last few years, with a wide scope of practical applications for electroencephalography (EEG). Transcutaneous electroacupuncture stimulation ...
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Sriniketan Sridhar, Anibal Romney and Vidya Manian
Mild Cognitive Impairment (MCI) and Alzheimer?s Disease (AD) are frequently associated with working memory (WM) dysfunction, which is also observed in various neural psychiatric disorders, including depression, schizophrenia, and ADHD. Early detection of...
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Diba Das, Mehdi Hasan Chowdhury, Aditta Chowdhury, Kamrul Hasan, Quazi Delwar Hossain and Ray C. C. Cheung
The electrooculogram (EOG) is one of the most significant signals carrying eye movement information, such as blinks and saccades. There are many human?computer interface (HCI) applications based on eye blinks. For example, the detection of eye blinks can...
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Andrei Velichko, Maksim Belyaev, Yuriy Izotov, Murugappan Murugappan and Hanif Heidari
Entropy measures are effective features for time series classification problems. Traditional entropy measures, such as Shannon entropy, use probability distribution function. However, for the effective separation of time series, new entropy estimation me...
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