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A direct connection between ElectroEncephaloGram (EEG) and the genetic information of individuals has been investigated by neurophysiologists and psychiatrists since 1960’s; and it opens a new research area in the science. This paper focuses on the person ...
Non-invasive brain-computer interfaces are traditionally based on mu rhythms, beta rhythms, slow cortical potentials or P300 event-related potentials. However, there is mounting evidence that neural oscillations up to 200 Hz play important roles in process ...
In this paper, we present a system for image replica detection. The technique is based on the extraction of 162 features corresponding to texture, colour and grey-level characteristics. These features are then weighted and statistically normalized. To impr ...
Spatial filtering is a widely used dimension reduction method in electroencephalogram based brain-computer interface systems. In this paper a new algorithm is proposed, which learns spatial filters from a training dataset. In contrast to existing approache ...
By directly analyzing brain activity, Brain-Computer Interfaces (BCIs) allow for communication that does not rely on any muscular control and therefore constitute a possible communication channel for the completely paralyzed. Typically, the user performs d ...
The inclusion of mental tasks transitions detection (MTTD) has proven a useful tool in guiding the transduction process of a BCI working under an asynchronous protocol. MTTD allows for the extraction of the signal's contextual information in order to infer ...
Melanoma is the most deadly skin cancer. Early diagnosis is a current challenge for clinicians. Current algorithms for skin lesions classification focus mostly on segmentation and feature extraction. This paper instead puts the emphasis on the learning pro ...
Brain-computer interfaces (BCIs) aim to provide a new channel of communication by enabling the subject to control an external systems by using purely mental commands. One method of doing this without invasive surgical procedures is by measuring the electri ...
Gradient boosting is a machine learning method, that builds one strong classifier from many weak classifiers. In this work, an algorithm based on gradient boosting is presented, that detects event-related potentials in single electroencephalogram (EEG) tri ...
In this paper, we present a system for image replica detection. More specifically, the technique is based on the extraction of 162 features corresponding to texture, color and gray-level characteristics. These features are then weighted and statistically n ...