The focus on smartphone-operated wearable devices for health and care allows for home-based applications with a high usability. 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.
#EEG SENSOR ANDROID#
Moreover, we recently showed that off-the-shelf Android smartphones can handle stimulus presentation as well as EEG acquisition on a single device. Head-mounted wireless EEG amplifiers in combination with small notebooks allow for EEG acquisition during natural motion, such as outdoor walking and cycling.
With the recently introduced small, head-mounted wireless EEG amplifiers and their confirmed applicability in real-life situations new paradigms for out-of-the-lab setups are now possible. 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. A clear drawback of current laboratory BCI technology is that the hardware is often bulky, stationary, and relatively expensive and thereby limits progress.įurthermore, established laboratory EEG recording technology does not easily allow for the investigating of brain correlates of natural human behaviour.
#EEG SENSOR DRIVER#
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. BCIs typically benefit from a machine learning signal processing approach. The aim is to identify cognitive states from EEG signatures in real time to exert control without any muscular involvement. Brain-computer interfaces (BCI) typically make use of EEG signals as well. 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. EEG signals refer to voltage fluctuations in the microvolt range and they are frequently acquired to address clinical as well as research questions.
IntroductionĮlectroencephalography (EEG) is a well-established approach enabling the noninvasive recording of human brain-electrical activity. 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. 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. We also validate SCALA with a well-established auditory selective attention paradigm and report above chance level classification results for all participants. We present timing test results supporting sufficient temporal precision of audio events. We have implemented the open source signal processing application SCALA. 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.
#EEG SENSOR SOFTWARE#
The software application SCALA (Signal ProCessing and CLassification on Android) supports a standardized communication interface to exchange information with external software and hardware. 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.