Pekka-Henrik Niemenlehto

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The analysis of eye movements has proven to be valuable in both clinical work and research as well as in other fields besides medicine. The detection of saccadic eye movements and the extraction of related saccade parameters, such as maximum angular velocity, amplitude, and duration, are usually performed during the analysis of electro-oculographic (EOG)(More)
A capacitive facial movement detection method designed for human-computer interaction is presented. Some point-and-click interfaces use facial electromyography for clicking. The presented method provides a contactless alternative. Electrodes with no galvanic coupling to the face are used to form electric fields. Changes in the electric fields due to facial(More)
A light-weight, wearable, wireless gaze tracker with integrated selection command source for human-computer interaction is introduced. The prototype system combines head-mounted, video-based gaze tracking with capacitive facial movement detection that enable multimodal interaction by gaze pointing and making selections with facial gestures. The system is(More)
The cell averaging constant false alarm rate technique was applied to the detection of saccades from electro-oculographic signals. The investigated saccade detection method achieves constant false alarm rate by adjusting its sensitivity according to the average noise level in the observed signal. Excluding the choice of certain parameters and(More)
The goal of this research was to investigate neural network-based methods to be applied in the processing of biomedical signals. We developed a neural network-based method for the detection of voluntarily produced changes in facial muscle action potentials. Electromyographic signals were recorded from the <i>corrugator supercilii</i> and <i>zygomaticus(More)
The present aim was to describe the first phase attempts to recognise voluntarily produced changes in electromyographic signals measured from two facial muscles. Thirty subjects voluntarily activated two facial muscles, corrugator supercilii and zygomaticus major. We designed a neural network based recognition system that screened out muscle activations(More)
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