Brain Computer Interface / BCI / Neural Control Int. / NCI / Mind Machine Int. / MMI / Direct Neural Int. / DNI / Brain Machine Int. / BMI
Sepide Khoneiveh; Ali Maleki
Volume 12, Issue 2 , September 2018, , Pages 161-171
Abstract
Steady state somatosensory evoked potential (SSSEP) is one of the control signals of brain-computer interfaces (BCI), based on the reflection of skin vibrational stimulation with specific frequencies in brain signals. BCI systems based on SSSEP do not cause visual fatigue in comparison with SSVEP based ...
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Steady state somatosensory evoked potential (SSSEP) is one of the control signals of brain-computer interfaces (BCI), based on the reflection of skin vibrational stimulation with specific frequencies in brain signals. BCI systems based on SSSEP do not cause visual fatigue in comparison with SSVEP based BCI systems, and they can be used for locked-in or amyotrophic lateral sclerosis (ALS) patients. So far, few studies have been done on SSSEP and its applications in BCI systems, because the hardware implementation of this system is challenging. In this paper, a vibrational stimulation device based on vibrational motor has been developed. This device has two separate output channels for applying vibrational stimulation to two different points of the body. The output frequency of each channel is adjustable in the range of 15 to 35 Hz with a step of 1 Hz. All parts of the device and the actuators have been shielded to prevent the emission of electromagnetic noise.
Reza Foodeh; Vahid Shalchyan; Mohammad Reza Daliri
Volume 10, Issue 3 , October 2016, , Pages 267-277
Abstract
Extracting discriminative features is a crucial step in brain-computer interfaces (BCIs) that could affect directly on the classification performance. Common spatial patterns (CSP) is a commonly used algorithm for such propose in motor imagery based BCI systems. CPS tries to extract the most appropriate ...
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Extracting discriminative features is a crucial step in brain-computer interfaces (BCIs) that could affect directly on the classification performance. Common spatial patterns (CSP) is a commonly used algorithm for such propose in motor imagery based BCI systems. CPS tries to extract the most appropriate spatial patterns in the electroencephalogram (EEG) signals to discriminate different motor imagery classes. Before applying CSP, Usually EEG signals are filtered out in 8-30 Hz to capture event related desynchronization (ERD) specific frequency rhythms called mu and beta bands. However, this frequency band could be highly subject specific. Therefore, optimizing spectral and spatial filters jointly could improve the classification accuracy. In this paper, we proposed a novel learning algorithm to derive spatial and spectral filters simultaneously using an evolutionary learning algorithm called particle swarm optimization (PSO). Furthermore, we utilized mutual information between extracted features and class labels as a cost function in the learning algorithm. Our simulations on BCI competition IV, dataset 1 reveals that the proposed method significantly outperforms the conventional CSP and filter bank CSP (FBCSP) with two different filter bank architectures.
