BackgroundLarge amounts of electro-oculographic (EOG) data, recorded during electroencephalographic (EEG) measurements, go underutilized. We present an automatic, auto-calibrating algorithm that allows efficient analysis of such data sets.MethodsThe auto-calibration is based on automatic threshold value estimation. Amplitude threshold values for saccades and blinks are determined based on features in the recorded signal. The performance of the developed algorithm was tested by analyzing 4854 saccades and 213 blinks recorded in two different conditions: a task where the eye movements were controlled (saccade task) and a task with free viewing (multitask). The results were compared with results from a video-oculography (VOG) device and manually scored blinks.ResultsThe algorithm achieved 93% detection sensitivity for blinks with 4% false positive rate. The detection sensitivity for horizontal saccades was between 98% and 100%, and for oblique saccades between 95% and 100%. The classification sensitivity for horizontal and large oblique saccades (10 deg) was larger than 89%, and for vertical saccades larger than 82%. The duration and peak velocities of the detected horizontal saccades were similar to those in the literature. In the multitask measurement the detection sensitivity for saccades was 97% with a 6% false positive rate.ConclusionThe developed algorithm enables reliable analysis of EOG data recorded both during EEG and as a separate metrics.

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The successful recording of neurophysiologic signals, such as event-related potentials (ERPs) or event-related magnetic fields (ERFs), relies on precise information of stimulus presentation times. We have developed an accurate and flexible audiovisual sensor solution operating in real-time for on-line use in both auditory and visual ERP and ERF paradigms. The sensor functions independently of the used audio or video stimulus presentation tools or signal acquisition system. The sensor solution consists of two independent sensors; one for sound and one for light. The microcontroller-based audio sensor incorporates a novel approach to the detection of natural sounds such as multipart audio stimuli, using an adjustable dead time. This aids in producing exact markers for complex auditory stimuli and reduces the number of false detections. The analog photosensor circuit detects changes in light intensity on the screen and produces a marker for changes exceeding a threshold. The microcontroller software for the audio sensor is free and open source, allowing other researchers to customise the sensor for use in specific auditory ERP/ERF paradigms. The hardware schematics and software for the audiovisual sensor are freely available from the webpage of the authors' lab.

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