Bad channel detection in EEG recordings

Abstract Electroencephalography (EEG) is widely used in clinical applications and basic research. Dry EEG opened the application area to new fields like self-application during gaming neurofeedback. While recording, signals are always affected by artefacts. Manual detection of bad channels gold standard both gel-based dry but timeconsuming. We propose a simple robust method for automatic channel EEG. Our based on iterative calculation deviations each channel. Statistical measures these serve as indications detection. compare results obtained from manually identified recordings. analysed resting state with eyes closed datasets head movement. The showed an accuracy 99.69 % our 99.38 movement setups. There was no significant difference between manual identification deviation method. Therefore, proposed can be