The event related P300 potentials, positive waveforms in electroencephalography (EEG) signals, are often utilized in brain computer interfaces (BCI). Many studies have been carried out to improve the performance of P300 speller systems either by developing signal processing algorithms and classifiers with different architectures or by designing new paradigms. In this study, a new paradigm is proposed for this purpose. The proposed paradigm combines two remarkable properties of being a 3D animation and utilizing column-only flashings as opposed to classical paradigms which are based on row-column flashings in 2D manner. The new paradigm is utilized in a traditional two-layer artificial neural networks model with a single output neuron, and numerous experiments are conducted to evaluate and compare the performance of the proposed paradigm with that of the classical approach. The experimental results, including statistical significance tests, are presented for single and multiple EEG electrode usage combinations in 1, 3 and 15 flashing repetitions to detect P300 waves as well as to recognize target characters. Using the proposed paradigm, the best average classification accuracy rates on the test data are improved from 89.97% to 93.90% (an improvement of 4.36%) for 1 flashing, from 97.11% to 98.10% (an improvement of 1.01%) for 3 flashings and from 99.70% to 99.81% (an improvement of 0.11%) for 15 flashings when all electrodes, included in the study, are utilized. On the other hand, the accuracy rates are improved by 9.69% for 1 flashing, 4.72% for 3 flashings and 1.73% for 15 flashings when the proposed paradigm is utilized with a single EEG electrode (P8). It is observed that the proposed speller paradigm is especially useful in BCI systems designed for few EEG electrodes usage, and hence, it is more suitable for practical implementations. Moreover, all participants, given a subjective test, declared that the proposed paradigm is more user-friendly than classical ones.
In this paper, mel-frequency cepstral coefficients are investigated for emotional content of speech signal. The features are extracted from spoken utterance. When these features are extracted, speech signal is divided small frames and each frame overlap a part of previous frame. The purpose of this overlap operation is to provide a smooth transition from one frame to the other and, to prevent information loss in the end of the frame. The length of frame and scroll time is important for emotion recognition applications. Also, we investigated the effects of different length frames and scroll times on the classification success of four emotions which are defined as happy, angry, neutral and sad. Those emotions were classified by using Support Vector Machine and kNearest Neighbors algorithms. In this study to determine the classification success, 10-Fold Cross Validation method was used and the maximum success rate was obtained as 98.7 %.
Cognitive fatigue is a discontinuous inability to maintain the existing cognitive performance and is a psycho-biological condition that occurs due to prolonged activities or working under stress. Cognitive fatigue causes an increase in errors, the emergence of various security vulnerabilities, and a decrease in performance. In this study, cognitive fatigue was tried to be determined by using EEG signals, which provide advantages in terms of use-transportation. Experiments were carried out with a total of 8 participants using the paradigm created for the detection of cognitive fatigue and EEG signals were recorded. Using the recorded EEG signals, the effects of different brain regions, different frequency bands, and different EEG lengths on the classification of cognitive workload were investigated. In addition, band power of EEG signals in situations with resting and cognitive workload were compared graphically. With the artificial neural network algorithm, the highest 99.49% classification accuracy was obtained by using the band power of the gamma frequency of all electrodes and the 5-second-long EEG segments.
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