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Research Article EEG Recording and Online Signal Processing on Android

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. 1. Introduction 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 . Brain-computer interfaces (BCI) typically make use of EEG signals as well . 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 . To name a few BCI applications, speller systems provide a communication channel for fully paralyzed individuals (e.g), motor imagery BCI systems promise controlling prostheses by thought alone , and BCI error monitoring systems have been shown to reliably detect car driver emergency braking intentions even before the car driver can hit a brake pedal, thereby supporting future braking assistance systems. A clear drawback of current Hindawi BioMed Research International Volume 2017, Article ID 3072870, 12 pages https://doi.org/10.1155/2017/3072870 2 BioMed Research International laboratory BCI technology is that the hardware is often bulky, stationary, and relatively expensive and thereby limits progress. Furthermore, established laboratory EEG recording technology does not easily allow for the investigating of brain correlates of natural human behaviour. EEG systems, as they are typically used in the lab, include wires connecting scalp electrodes and bulky amplifiers and they do not tolerate human motion during signal acquisition very well. With the recently introduced small, head-mounted wireless EEG amplifiers and their confirmed applicability in reallife situations new paradigms for out-of-the-lab setups are now possible. Head-mounted wireless EEG amplifiers in combination with small notebooks allow for EEG acquisition during natural motion, such as outdoor walking and cycling . Moreover, we recently showed that off-the-shelf Android smartphones can handle stimulus presentation as well as EEG acquisition on a single device. The combination of unobtrusive EEG sensors , wireless EEG amplifiers, and smartphone-based signal acquisition and stimulus presentations (which we call transparent EEG ) opens up a plethora of possibilities for research, diagnostics, and therapy. The focus on smartphone-operated wearable devices for health and care allows for homebased applications with a high usability. Smartphone are ubiquitous and socially accepted and provide an unparalleled flexibility. Current smartphone technologies provide sufficient computing power to implement all the steps required for a BCI on a single device, but few groups have attempted to explore this possibility . In previous studies we have shown that Android smartphone-based EEG recordings as well as stimulus presentation on the phone or on a tablet are feasible. However, while the signal quality achieved on handheld devices may be comparable to previous desktop computer-recorded EEG signals, all signal processing and classification routines were applied offline on desktop computers, after signal acquisition was concluded. Also, in Debener et al. The temporal precision of auditory events lacked laboratory standard millisecond precision. Debener et al.Reported a temporal jitter of approximately 6 ms standard deviation. Stopczynski et al. Pioneered an online EEG acquisition and source modelling system running on Android devices. The Smartphone Brain Scanner project is freely available and includes real-time visualization of ongoing EEG activity in source space. While confirming the general practicability of on-smartphone processing, the system does not consider delays and processing overheads, as more general processing frameworks would do, and it does not provide a general framework for precise stimulus control and presentation of stimuli, as it is typically required for the implementation of BCI applications. A further drawback is that the Smartphone Brain Scanner requires a rooted smartphone and a custom kernel. Another group presented the NeuroPhone, a BCI application on iPhone. However, while the iPh

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