⭐⭐⭐⭐⭐ SSVEP-EEG Signal Classification based on Emotiv EPOC BCI and Raspberry Pi
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The document discusses the development of a low-cost device using Emotiv Epoc BCI and Raspberry Pi for real-time SSVEP-EEG signal classification to assist individuals with disabilities. It details the experimental methodology, dataset organization, data preprocessing, and classification techniques, highlighting the challenges faced in achieving accuracy amid noisy signals. Results indicate improvement in classification accuracy with data augmentation, and suggestions for future work include larger participant recruitment and the application of deep learning techniques.
![● Zhang et al., 2015 proposed to analize beta wave for a range of [5-20] Hz in the
front parietal and occipital regions. This requires more computational capacity
for the data processing device.
● Khosla et al., 2020 proposed feature selection methods in order to choose
relevant features that contribute to a successful classification of the user’s
intentions, resulting in an increase in the quality of the later results.
● Han et al., 2018 proposed to focus on the activity of the occipital and parietal
regions of the subject, in order to obtain a high classification rate.
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⭐⭐⭐⭐⭐ SSVEP-EEG Signal Classification based on Emotiv EPOC BCI and Raspberry Pi
- 1. SSVEP-EEG Signal Classification based on Emotiv EPOC BCI and Raspberry Pi Karla Avilés-Mendoza , Víctor Asanza , Hector Trivino-Gonzalez, Félix Rosales-Uribe, Jamil Torres-Brunes, Francis R. Loayza, Enrique Peláez, Ricardo Cajo and Raquel Tinoco- Egas Escuela Superior Politécnica del Litoral, ESPOL, Guayaquil, Ecuador Facultad de Ingeniería en Electricidad y Computación, FIEC
- 2. Content Introduction Dataset • Organizing the experiment • Acquisition device • Experimental methodology Results Methodology • Data pre-processing • Feature extraction • Data normalization • Classification configuration 01 02 03 04 Conclusions 05
- 4. 1 billion According to OMS over people live with some form of disability, this is about 15% of the world’s population.
- 5. ● First approach for a low cost device capable of processing EEG signals in real-time to control an external actuator. ● More affordability for people with low economic respurces. ● Improve response time. ● Visual stimuli comming from a mobile device, eg: to control a wheelchair, a robotic arm, among others. ● To achieve a high accuracy with low computational cost. ● Accesible and easy to use. ● To achieve a aceptable accuracy with a faster pre-processing and classification time. ● The light of a mobile device is not that intense in comparison to light bulbs. Motivation Challenges
- 6. ● Zhang et al., 2015 proposed to analize beta wave for a range of [5-20] Hz in the front parietal and occipital regions. This requires more computational capacity for the data processing device. ● Khosla et al., 2020 proposed feature selection methods in order to choose relevant features that contribute to a successful classification of the user’s intentions, resulting in an increase in the quality of the later results. ● Han et al., 2018 proposed to focus on the activity of the occipital and parietal regions of the subject, in order to obtain a high classification rate. Related work
- 7. AI techniques • Reduce the complexity of noisy data. • Increase data classification accuracy. • Feature extraction using temporary windows. • Reduce costs and improves chances of usage. BCI based on EEG • Brain waves are use to communicate the user’s intentions to external actuators. • Non-invasive electroencephalography (EEG) techniques. • Low – cost devices, more accesible
- 8. Challenges • High error rate. • Multiclass EEG data classification techniques. • Data acquisition is highly noise susceptible: a blink, a small movement of the scalp, hair, adipose tisssue, among others can cause noise. SSVEP • Evoked potential produced by a visual stimuli flashing at a specific frecuency. • Between 6-75 Hz
- 9. Dataset 02 Dataset Citation: Raquel Tinoco-Egas, Karla Aviles, Jamil Torres-Brunes, Hector Trivino-Gonzalez, Víctor Asanza, Félix Rosales-Uribe, Francis R. Loayza, Enrique Peláez, April 27, 2021, "SSVEP-EEG data collection using Emotiv EPOC", IEEE Dataport, doi: https://dx.doi.org/10.21227/0j42-qd38.
- 10. Experiment organization ● 20 adult subjects were recruited between the age of 20 – 35. ● Staff avoided wearing brightly colored clothes that could distract the subjects. ● Staff respected COVID biosecurity measures. ● Temperature: 25 degrees Celsius. ● Noise: 30 – 55 decibels (air conditioning and car passing through) ● Participants signed an informed consent.
- 11. Acquisition device ● Emotiv EPOCx ● Sampling frecuency: 128 Hz ● 14 electrodes (2 ground references) - International 10-10 eeg system ● Conductive gel to reduce impedance between the electrodes and the scalp
- 13. ● Tasks duration: 3.5 seconds ● Frecuency tasks were shown 40 times each. ● When a frecuency task was being shown, the other squares turned opaque by 80%. Experimental methodology
- 14. Experimental methodology Keys Emotiv Label Class Q 1 7 Hz W 2 9 Hz E 3 11 Hz R 4 13 Hz T 5 Baseline
- 15. Methodology 03
- 16. Data pre-processing ● Occipital region – electrodes: O1 and O2. ● The single output files were divided into several files containing a temporary window. ● Files were splitted into folders representing their respective frecuency. ● A Butterworth filter of order 20 was applied. Frecuency limit: 5 Hz – 30 Hz. ● Outliers were extracted.
- 17. Data pre-processing – Data augmentation ● Data augmentation was used by applying White noise of different amplitudes to the data. ● White noise: randomly generated array of values added to the signal, so it doesn’t lose the general behavior but the values do change. Small amplitude white noise Big amplitude white noise
- 18. Feature extraction ● Extracted 21 features. • Mean • Mean - weight I • Mean - weight II • Log Detector • Median • Variance • Mean absolute difference • Mean frecuency • Peak frecuency • Variance central frecuency • Maximum PSD • Amplitude Histogram (10 ranges)
- 19. Data normalization ● Sklearn MinMaxScaler. 1. Normalize training data and save minimum and maximum values 2. Normalize validation and test data using minimum and maximum values from the training data.
- 20. Classification configuration ● Support Vector Machine (SVM) ● Multilayer Perceptron (MLP) ● Random Forest (RF) ● k-Nearest Neighbors (KNN) ● eXtreme Gradient Boosting (XGBoost)
- 21. Results 04
- 24. Conclusions 05
- 25. ● Without data augmentation RF and MLP whose accuracy values were 54% and 52%, respectively. ● With data augmentation RF and XGBoost whose accuracy values ware 58% and 57%, respectively. ● Over-adjustment in the classification algorithms, due to the limited number of examples. ● Real-time responses, shorter classification time were MLP and XGBoost with times of 1.8 and 7.12 milliseconds, respectively. ● Adequate experimental design, because cleaner data will help to improve the classification. ● Recruit more subjects to eliminate over-adjustment in the algorithms. ● Deep Learning techniques (DL) and spectral images.
- 26. [email protected] karla-aviles-mendoza Do you have any questions? Víctor Asanza [email protected] vasanza Karla Avilés
- 27. Thanks