Abstract
Objective Our aim was the development and validation of a modular signal processing and classification application enabling online electroencephalography (EEG) signal processing on off-the-shelf mobile Android devices. The software application SCALA (Signal ProCessing and CLassification on Android) supports a standardized communication interface to exchange information with external software and hardware. Approach In order to implement a closed-loop brain-computer interface (BCI) on the smartphone, we used a multiapp framework, which integrates applications for stimulus presentation, data acquisition, data processing, classification, and delivery of feedback to the user. Main Results We have implemented the open source signal processing application SCALA. We present timing test results supporting sufficient temporal precision of audio events. We also validate SCALA with a well-established auditory selective attention paradigm and report above chance level classification results for all participants. Regarding the 24-channel EEG signal quality, evaluation results confirm typical sound onset auditory evoked potentials as well as cognitive event-related potentials that differentiate between correct and incorrect task performance feedback. Significance We present a fully smartphone-operated, modular closed-loop BCI system that can be combined with different EEG amplifiers and can easily implement other paradigms.
Highlights
Electroencephalography (EEG) is a well-established approach enabling the noninvasive recording of human brain-electrical activity
Aim of this project was to foster the development of Brain-computer interfaces (BCI) applications for Android smartphones
To this end we developed and evaluated SCALA, a modular BCI software solution for the processing of physiological time series data
Summary
Electroencephalography (EEG) is a well-established approach enabling the noninvasive recording of human brain-electrical activity. EEG signals refer to voltage fluctuations in the microvolt range and they are frequently acquired to address clinical as well as research questions. Many studies in the research field of cognitive neuroscience rely on EEG, since EEG hardware is available at relatively low cost and EEG signals enable to capture the neural correlates of mental acts such as attention, speech, or memory operations with millisecond precision [1]. Typically make use of EEG signals as well [2]. The aim is to identify cognitive states from EEG signatures in real time to exert control without any muscular involvement. BCIs typically benefit from a machine learning signal processing approach [3].
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