|
|
|
|
|
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...
ver más
|
|
|
|
|
|
|
|
|
|
|
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...
ver más
|
|
|
|
|
|
|
|
|
|
|
Mashael Aldayel, Amira Kharrat and Abeer Al-Nafjan
Individual choices and preferences are important factors that impact decision making. Artificial intelligence can predict decisions by objectively detecting individual choices and preferences using natural language processing, computer vision, and machin...
ver más
|
|
|
|
|
|
|
|
|
|
|
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...
ver más
|
|
|
|
|
|
|
|
|
|
|
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...
ver más
|
|
|
|
|
|
|
|
|
|
|
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 ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Marcin Kolodziej, Andrzej Majkowski, Remigiusz J. Rak and Przemyslaw Wiszniewski
One approach employed in brain?computer interfaces (BCIs) involves the use of steady-state visual evoked potentials (SSVEPs). This article examines the capability of artificial intelligence, specifically convolutional neural networks (CNNs), to improve S...
ver más
|
|
|
|
|
|
|
|
|
|
|
Dmitry Lazurenko, Igor Shepelev, Dmitry Shaposhnikov, Anton Saevskiy and Valery Kiroy
A linear discriminant analysis transformation-based approach to the classification of three different motor imagery types for brain?computer interfaces was considered. The study involved 16 conditionally healthy subjects (12 men, 4 women, mean age of 21....
ver más
|
|
|
|
|
|
|
|
|
|
|
Andrea Sanna, Federico Manuri, Jacopo Fiorenza and Francesco De Pace
Human?robot collaboration (HRC) is a new and challenging discipline that plays a key role in Industry 4.0. Digital transformation of industrial plants aims to introduce flexible production lines able to adapt to different products quickly. In this scenar...
ver más
|
|
|
|
|
|
|
|
|
|
|
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...
ver más
|
|
|
|
|
|