On the Use of FOOOF for Electroencephalography Quality Measurement and Device Assessment

Electroencephalography (EEG) signals capture the electrical activity of the brain and have traditionally been used in clinical settings. More recently, with the development of mobile, wearable EEG devices their use has been explored for other applications, including emotion recognition, quality of experience monitoring, or fatigue detection, just to name a few. EEG signals are known to have a 1/f-noise like structure and are very low amplitude, thus making them highly susceptible to artefacts, such as power line interference, muscle movement, and eye blinks. Moreover, prototyping and development of new devices to record EEG signals may introduce additional sources of artefacts generated by different instrumentation settings. As such, automated quantification of the quality of the EEG signals has become important and the focus of recent research. Here, we propose a new quality metric based on the 1/f-noise structure of the EEG signal. Experimental results show the proposed metric classifying clean versus noisy EEG segments in a subject-independent setting with an accuracy of 86.0% for the AF7 electrode location and 64.6% for AF8, two electrode locations known to be highly degraded by artefacts. Additionally, the proposed metrics are shown to generalize well to unseen electrode locations. For example, a quality model trained on AF7 noisy EEG data achieved an accuracy of 61.4% when tested on data collected from the AF8 location. EEG, quality, fooof, prototyping