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Egor I. Chetkin, Sergei L. Shishkin and Bogdan L. Kozyrskiy
Bayesian neural networks (BNNs) are effective tools for a variety of tasks that allow for the estimation of the uncertainty of the model. As BNNs use prior constraints on parameters, they are better regularized and less prone to overfitting, which is a s...
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Georgios Prapas, Kosmas Glavas, Katerina D. Tzimourta, Alexandros T. Tzallas and Markos G. Tsipouras
Brain-computer interfaces (BCIs) are becoming an increasingly popular technology, used in a variety of fields such as medical, gaming, and lifestyle. This paper describes a 3D non-invasive BCI game that uses a Muse 2 EEG headband to acquire electroenceph...
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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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Sepideh Kilani, Seyedeh Nadia Aghili and Mircea Hulea
A new approach is introduced to address the subject dependency problem in P300-based brain-computer interfaces (BCI) by using transfer learning. The occurrence of P300, an event-related potential, is primarily associated with changes in natural neuron ac...
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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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Diego F. Collazos-Huertas, Andrés M. Álvarez-Meza and German Castellanos-Dominguez
Brain activity stimulated by the motor imagery paradigm (MI) is measured by Electroencephalography (EEG), which has several advantages to be implemented with the widely used Brain?Computer Interfaces (BCIs) technology. However, the substantial inter/intr...
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Ali Aroudi, Eghart Fischer, Maja Serman, Henning Puder and Simon Doclo
Recent advances have shown that it is possible to identify the target speaker which a listener is attending to using single-trial EEG-based auditory attention decoding (AAD). Most AAD methods have been investigated for an open-loop scenario, where AAD is...
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Shang Feng, Haifeng Li, Lin Ma and Zhongliang Xu
In the application of the brain-computer interface, feature extraction is an important part of Electroencephalography (EEG) signal classification. Using sparse modeling to extract EEG signal features is a common approach. However, the features extracted ...
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Grégoire Cattan, Anton Andreev and Etienne Visinoni
The integration of a P300-based brain?computer interface (BCI) into virtual reality (VR) environments is promising for the video games industry. However, it faces several limitations, mainly due to hardware constraints and limitations engendered by the s...
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Steven Galindo-Noreña, David Cárdenas-Peña and Álvaro Orozco-Gutierrez
Brain?computer interface (BCI) systems communicate the human brain and computers by converting electrical activity into commands to use external devices. Such kind of system has become an alternative for interaction with the environment for people suffer...
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Mashael Aldayel, Mourad Ykhlef and Abeer Al-Nafjan
This article presents an application of deep learning in preference detection performed using EEG-based BCI.
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Feng Li, Xiaoyu Li, Fei Wang, Dengyong Zhang, Yi Xia and Fan He
Aiming at enhancing the classification accuracy of P300 Electroencephalogram signals in a non-invasive brain?computer interface system, a novel P300 electroencephalogram signals classification algorithm is proposed which is based on improved convolutiona...
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Andrey N. Afonin,Rustam G. Asadullaev,Maria A. Sitnikova,Andrey R. Gladyshev,Kamil Kh. Davletchurin
The review describes the main principles as well as advantages and disadvantages of the modern brain-computer interfaces applied in robotic devices. The invasive and non-invasive devices based on the origin of a signal, invasiveness and location of probe...
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Saraswati Sridhar and Vidya Manian
Cognitive deterioration caused by illness or aging often occurs before symptoms arise, and its timely diagnosis is crucial to reducing its medical, personal, and societal impacts. Brain?computer interfaces (BCIs) stimulate and analyze key cerebral rhythm...
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Christoph Reichert, Stefan Dürschmid, Rudolf Kruse and Hermann Hinrichs
Brain?computer interfacing (BCI) is a promising technique for regaining communication and control in severely paralyzed people. Many BCI implementations are based on the recognition of task-specific event-related potentials (ERP) such as P300 responses. ...
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Woo-Sung Choi and Hong-Gi Yeom
A brain?computer interface (BCI) is a promising technology that can analyze brain signals and control a robot or computer according to a user?s intention. This paper introduces our studies to overcome the challenges of using BCIs in daily life. There are...
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Grégoire Cattan, Cesar Mendoza, Anton Andreev and Marco Congedo
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Eduardo Carabez, Miho Sugi, Isao Nambu and Yasuhiro Wada
As brain-computer interfaces (BCI) must provide reliable ways for end users to accomplish a specific task, methods to secure the best possible translation of the intention of the users are constantly being explored. In this paper, we propose and test a n...
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Hikaru Sato and Yoshikazu Washizawa
A brain-computer interface (BCI) is a tool to communicate with a computer via brain signals without the user making any physical movements, thus enabling disabled people to communicate with their environment and with others. P300-based ERP spellers are a...
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