Ecoc based multi-class classification in brain computer interfaces with ssvep
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Saghir, Sandra (2020) Ecoc based multi-class classification in brain computer interfaces with ssvep. [Thesis]
Official URL: https://risc01.sabanciuniv.edu/record=b2553781_(Table of contents)
Brain-Computer Interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) responses are among the most frequently used non-invasive BCI systems due to their feasibility, portability, and low cost. SSVEPs are the brain responses to flickering visual stimuli at a specific frequency. One of SSVEP’s critical applications is SSVEP-based BCI speller; this system allows disabled people to communicate directly by using their brain signals without dependence on speech production. An SSVEP-based BCI speller incorporates a variety of flickering characters or numbers. Therefore, decoding brain activities for an SSVEP-based BCI speller requires solving a multi-class classification problem. Over the last few years, various studies have attempted to achieve higher frequency recognition accuracy and faster information transfer rates to enhance the recognition performance. This thesis employs an ensemble method called Error-Correcting Output Codes (ECOC) to tackle the above-mentioned multi-class classification problem. To the best of our knowledge, the ECOC framework has not been explored for the SSVEP classification problems to date. We present an extensive set of comparisons among four prominent ECOC coding matrix designs, one-vs-all (OVA), one-vs-one (OVO), random dense, and random sparse. Furthermore, three feature extraction methods are investigated to evaluate the overall performance of such designs. The utilized feature extraction methods include Canonical Correlation Analysis (CCA), Power Spectrum Density Analysis (PSDA) via Welch’s method, and Correlated Components Analysis (CORRCA). Using the ECOC ensemble method improves the general performance compared to the standard methods such as standard CCA and standard CORRCA. Moreover, the results indicate the superiority of the feature extraction method CORRCA especially for a short time window and the OVA coding matrix design. In conclusion, The presented approach has the ability to incorporate high-performance BCI speller systems based on SSVEP.
|Uncontrolled Keywords:||Steady state visually evoked potentials (SSVEP). -- brain-computer interfaces (BCI). -- electroencephalography (EEG). -- error correcting output codes (ECOC). -- multi-class classification. -- Duragan hal görsel uyarılmıs potansiyel (DHGUP). -- beyin-bilgisayar arayüzü (BBA). -- elektroensefalografi (EEG). -- Hataya Dayanıklı Çıktı Kodları (HDÇK). -- çok sınıflı sınıflandırma.|
|Subjects:||T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics |
|Deposited On:||03 May 2021 14:39|
|Last Modified:||03 May 2021 14:39|
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