|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|383542||660826||2016||10 صفحه PDF||سفارش دهید||دانلود رایگان|
• A binary-constrained version of the Flower Pollination Algorithm has been proposed.
• Sensor selection in EEG signals by means of optimization techniques.
• To evaluate the proposed approach in the context of biometrics.
Electroencephalogram (EEG) signal presents a great potential for highly secure biometric systems due to its characteristics of universality, uniqueness, and natural robustness to spoofing attacks. EEG signals are measured by sensors placed in various positions of a person’s head (channels). In this work, we address the problem of reducing the number of required sensors while maintaining a comparable performance. We evaluated a binary version of the Flower Pollination Algorithm under different transfer functions to select the best subset of channels that maximizes the accuracy, which is measured by means of the Optimum-Path Forest classifier. The experimental results show the proposed approach can make use of less than a half of the number of sensors while maintaining recognition rates up to 87%, which is crucial towards the effective use of EEG in biometric applications.
Journal: Expert Systems with Applications - Volume 62, 15 November 2016, Pages 81–90