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Brain Machine Interface: BCI Problems and Solutions
- 1. Brain Machine Interface Alaa Joukhadar – Huda Saghergy – Leen Kwieder
- 3. BCI Problems Slow Transfer RateWeak Signals Inaccurate Classification of signals 3 High Error
- 4. 1. STEPS
- 6. STEP 1. Signal Acquisition Invasive Acquisition . Partially Invasive Acquisition . Non Invasive Acquisition 6
- 9. 1. Signal Improvements: Signal Artifacts 9 Extra-Physiological Artifacts Alternating Current Artifact Movements In Recording Environment Interface With Another Equipment Physiological Artifacts: EOG ECG EMG
- 10. Extra-Physiological Artifacts: Extra-Physiological Artifacts ∙Alternating Current Artifact ∙It happens when there is no sufficient grounding for the EEG cap electrodes ∙Electrode Artifact Incorrect placement of the electrode on the patient’s head ∙Movements in Recording Environment ∙Movement of other people around the patient 10
- 11. Physiological Artifacts: Physiological Artifacts ∙EOG ∙It occurs from eye movement functionality during EEG recording ∙ECG Caused by the interface of the heartbeats and moves potentials over patient’s scalp surface ∙EMG ∙Generated by clenching of jaw muscles. 11
- 12. 1. Signal Improvements: Signal Improvement 12 Linear FilteringAvoidance Quantum Neural Network Cauchy Filter
- 13. Signal Improvement ∙Avoidance ∙Technical preparation for acquisition ∙Quantum Neural Network Based filter Recurrent quantum neural network improves the acquired EEG signal quality (Like QRNN) 13
- 14. Signal Improvement ∙Linear Filtering ∙Low pass filter & high pass filter (No Overlapping) ∙Cauchy Filter Capable of filtering EOG and EMG artifacts especially at medical applications such as Epilepsy Seizures. 14
- 15. 2. Feature Extraction: 15 WaveletRaw EEG Fourier Transform PCA Too many features!!
- 16. Feature Extraction ∙Raw EEG ∙Infrequently used due to the amount of data features ∙Fast Fourier Transform (FFT) As with Raw EEG, FFT contains large amounts of data. 16
- 17. Feature Extraction ∙Wavelet ∙Concession between time- domain and frequency domain. ∙PCA Reduces feature dimensions. 17 Discrete Continuous
- 19. Classification Algorithms: 19 Linear Discriminant Analysis SVM CNN RNN 66% 69% 69%
- 20. Applications 20
- 26. Future & Challenges: 26 • Not enough understanding of the brain • Bringing BMI applications into the real world. • Signal processing • Having hardware the suits the users environment.