Yazar "Sozer, Abdullah Talha" seçeneğine göre listele

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    Enhanced Single Channel SSVEP Detection Method on Benchmark Dataset
    (Ieee, 2018) Sozer, Abdullah Talha
    Steady state visual evoked potential (SSVEP) is a brain response that allows a practical and high-performance brain-computer interface (BCI) to be designed. SSVEP response is a near sinusoidal waveform at a visual stimulus frequency and is time-locked to stimulus onset. This paper presents a new single channel SSVEP detection method that takes advantage of the behaviour of SSVEP response. The proposed method defines subject-specific sinusoids at the training stage. Detection of a target stimulus frequency is achieved by a correlation value between the electroencephalography (EEG) signal and subject specific sinusoids at the test stage. The performance of the developed method was compared with the well-known power spectral density analysis (PSDA) on a benchmark dataset. Experimental results show that the developed method significantly improves the SSVEP detection accuracy (by about 23%) as well as the information transfer rate (ITR) compared to PSDA methods.
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    Implementation of a Steady State Visual Evoked Potantial Based Brain Computer Interface
    (Ieee, 2016) Sozer, Abdullah Talha; Fidan, Can Bulent
    In this study, steady-state visual evoked potential based brain computer interface design and implementation was being carried out. A portable and affordable EEG device was used to obtain brain signals. Computer monitor were preferred as visual stimuli source. In offline and online experiment, for detection of target visual stimulus selected by user, the amplitude of the EEG signal components, which correspond visual stimulus frequencies, and correlation coefficient features were tested. In the classification stage the performance of different classification methods were compared and presented along tables.
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    Novel spatial filter for SSVEP-based BCI: A generated reference filter approach
    (Pergamon-Elsevier Science Ltd, 2018) Sozer, Abdullah Talha; Fidan, Can Bulent
    Steady state visual evoked potential (SSVEP)-based brain computer interface (BCI) systems can be realised using only one electrode; however, due to the inter-user and inter-trial differences, the handling of multiple electrode is preferred. This raises the problem of evaluating information from multiple electrode signals. To solve this problem, we developed a novel spatial filtering method (Generated Reference Filter) for SSVEP-based BCIs. In our method an artificial reference signal is generated by a combination of reference electrode signals. Multiple regression analysis (MRA) was used to determine the optimal weight coefficients for signal combination. The filtered signal was obtained by subtraction. The method was tested on a SSVEP dataset and compared with minimum energy combination and common reference methods, namely the surface Laplacian technique and common average referencing. The newly developed method provided more effective filtering and therefore higher SSVEP detection accuracy was obtained. It was also more robust against subject-to-subject and trial-to-trial variability as the artificial reference signal was recalculated for each detection round. No special preparation is required, and the method is easy to implement. These experimental results indicate that the proposed method can be used confidently with SSVEP-based BCI systems.