Visual perception affected by motivation and alertness controlled by a noninvasive brain-computer interface

Abstract

The influence of motivation and alertness on brain activity associated with visual perception was studied experimentally using the Necker cube, which ambiguity was controlled by the contrast of its ribs. The wavelet analysis of recorded multichannel electroencephalograms (EEG) allowed us to distinguish two different scenarios while the brain processed the ambiguous stimulus. The first scenario is characterized by a particular destruction of alpha rhythm (8–12 Hz) with a simultaneous increase in beta-wave activity (20–30 Hz), whereas in the second scenario, the beta rhythm is not well pronounced while the alpha-wave energy remains unchanged. The experiments were carried out with a group of financially motivated subjects and another group of unpaid volunteers. It was found that the first scenario occurred mainly in the motivated group. This can be explained by the increased alertness of the motivated subjects. The prevalence of the first scenario was also observed in a group of subjects to whom images with higher ambiguity were presented. We believe that the revealed scenarios can occur not only during the perception of bistable images, but also in other perceptual tasks requiring decision making. The obtained results may have important applications for monitoring and controlling human alertness in situations which need substantial attention. On the base of the obtained results we built a brain-computer interface to estimate and control the degree of alertness in real time.

Artificial Neural Network Classification of Motor‐Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity

“…The time-frequency analysis was performed with the help of wavelet decomposition in three frequency bands: delta (1-5 Hz), alpha (8)(9)(10)(11)(12), and beta (15-30 Hz) (see Material and Methods for detailed description). In every band, the wavelet energy was calculated for each movement type 5 Complexity by its averaging over all corresponding EEG trials.…”

“…However, the treatment of multichannel EEG signals is a very sophisticated task because they are nonstationary, high-dimensional, and extremely noisy [7,8]. All these factors make difficult the recognition and classification of specific motor-related or percept-related patterns in a single trial mode [9,10] and require extensive statistical measures.…”

Neuronal adaptation in the course of the prolonged task improves visual stimuli processing

“…Half of them (a ∈ {0.0, 0.15, 0.4, 0.45}) are considered as left-oriented (LO) and another half (a ∈ {0.55, 0.6, 0.85, 1}) as right-oriented (RO). While for a ≈ 0 and a ≈ 1 (low ambiguous (LA) images) the cubes can easily be interpreted as left-and right-oriented, for a ≈ 0.5 the identification of the cube orientation is a more difficult task since we deal with highly ambiguous (HA) images (Hramov et al (2018); Maksimenko et al (2017)). The 14.2-cm Necker cubes were drawn by black and gray lines on a white background at the center of a 24" BenQ LCD monitor with a spatial resolution of 1920 × 1080 pixels and a 60-Hz refresh rate.…”

Потоковая Обработка Многоканальных Сигналов Электроэнцефалограммы С Использованием Технологии Параллельных Вычислений На Графических Процессорах NVIDIA CUDA

“…В настоящей работе метод анализа ЭЭГ, основанный на парал-лельных вычислениях, апробируется на примере ИМК для оценки и контроля уровня концентрации внимания [18]. Оригинальный ИМК использовал в качестве входных параметров пять каналов ЭЭГ в области затылочной коры.…”