This folder contains personal notes, summaries, and project materials from the Ibn Sina Neurotech Summer School 2024, focusing on EEG-based Brain-Computer Interfaces (BCIs), signal processing, and machine learning applications in healthcare and neuroscience. The training involved hands-on sessions, tutorials, workshops and a mini-project carried out to gain practical experience in neurotechnology research.
- Institution: The American University in Cairo, School of Sciences and Engineering
- Dates: 8–12 September 2024
- Support: International Brain Research Organization (IBRO)
- Organizers: Arabs in Neuroscience
- Event Format: On-site Summer School
- Event Link:Ibn Sina Neurotech Summer School
- Main Speakers:
| Speaker | Affiliation |
|---|---|
| Prof. Dr. Seif Eldawlalty | The American University in Cairo, Egypt |
| Dr. Mohamed Zaky | Arab Academy for Science, Technology and Maritime Transport, Egypt |
| Eng. Mai Gamal | German University in Cairo, Egypt |
| Prof. Dr. Mahmoud Hassan | Reykjavik University, Iceland |
| Prof. Dr. Abdelkader Nasreddine | United Arab Emirates University, UAE |
| Dr. Mohamed Abdelhack | The Centre for Addiction and Mental Health (CAMH), Canada |
| Dr. Nour El-Madany | Ryerson University, Canada |
| Dr. Slobodan Tanackovic | g.tec medical engineering GmbH, Austria |
The Ibn Sina Neurotech Summer School introduced participants to neurotechnology, EEG-based BCIs, signal processing techniques, and machine learning applications in healthcare. The program combined theoretical sessions with hands-on workshops, providing participants with:
- Hands-on EEG recording and BCI experimental design
- Signal processing and feature extraction
- Machine learning applications for EEG classification
- Exposure to ethical considerations and neurotechnology innovation
- Participation in mini-projects with practical datasets
Detailed session notes are available in the Event-Notes folder.
Ibn-Sina-Neurotech-Summer-School-2024/
│
├── README.md # Overview of the event and project
├── Event-Notes/
│ ├── README.md # Summary of event notes
│ ├── Event-Notes-Day1.md # Notes from Day 1
│ ├── Event-Notes-Day2.md # Notes from Day 2
│ ├── Event-Notes-Day3.md # Notes from Day 3
│ ├── Event-Notes-Day4.md # Notes from Day 4
│ └── Event-Notes-Day5.md # Notes from Day 5
└── Project/
├── README.md # Overview of the project
├── SSVEP-CCA-Analysis-Report.md # Main report
├── presentation/
│ └── SSVEP-CCA-Project-Presentation.pdf # Final slides summarizing project
├── scripts/
│ └── SSVEP_CCA_Analysis_Project_Notebook.ipynb # Jupyter Notebook with analysis code
├── results/
│ └── cca_ssvep_results.xls # Excel sheet with final results
├── figures/
│ ├── .gitkeep
│ ├── accuracy_boxplot.png # Accuracy comparison across subjects
│ ├── accuracy_per_subject.png # Accuracy per individual subject
│ ├── confusion_matrix_last_subject.png # Confusion matrix for last subject
│ ├── max_cca_corr_hist_last_subject.png # Histogram of max CCA correlation
│ ├── sample_eeg_signal.png # Example EEG signal from dataset
│ └── true_vs_predicted_labels.png # True vs predicted labels plot
└── data/
├── s1.mat # EEG data for Subject 1 — MATLAB .mat file
├── s2.mat # EEG data for Subject 2 — MATLAB .mat file
├── s3.mat # EEG data for Subject 3 — MATLAB .mat file
├── s4.mat # EEG data for Subject 4 — MATLAB .mat file
├── s5.mat # EEG data for Subject 5 — MATLAB .mat file
├── s6.mat # EEG data for Subject 6 — MATLAB .mat file
├── s7.mat # EEG data for Subject 7 — MATLAB .mat file
├── s8.mat # EEG data for Subject 8 — MATLAB .mat file
├── s9.mat # EEG data for Subject 9 — MATLAB .mat file
└── s10.mat # EEG data for Subject 10 — MATLAB .mat file
| Folder / File | Content Description | Link |
|---|---|---|
| README.md | Overview of the Summer School, project, and skills | README.md |
| Event-Notes/ | Session notes, tutorials, workshops | Event-Notes |
| ├── Event-Notes-Day1.md | Intro to neurotechnology, EEG basics, SSVEP, P300 | Day1 |
| ├── Event-Notes-Day2.md | EEG preprocessing, ICA, artifact removal | Day2 |
| ├── Event-Notes-Day3.md | Feature extraction, ERP & SSVEP analysis | Day3 |
| ├── Event-Notes-Day4.md | Machine learning for EEG, CCA, SVM | Day4 |
| └── Event-Notes-Day5.md | Future directions, ethics, project presentations | Day5 |
| Project/ | EEG analysis project resources | Project |
| ├── SSVEP-CCA-Analysis-Report.md | Main project report | Report |
| ├── presentation/ | Project presentation slides | Presentation |
| ├── scripts/ | Jupyter Notebook with EEG preprocessing & CCA analysis | Notebook |
| ├── results/ | Final analysis results | Results |
| ├── figures/ | Graphs & visualizations supporting analysis | Figures |
| │ ├── accuracy_boxplot.png | Accuracy comparison across subjects | Link |
| │ ├── accuracy_per_subject.png | Accuracy per individual subject | Link |
| │ ├── confusion_matrix_last_subject.png | Confusion matrix for last subject | Link |
| │ ├── max_cca_corr_hist_last_subject.png | Histogram of max CCA correlation | Link |
| │ ├── sample_eeg_signal.png | Example EEG signal from dataset | Link |
| │ └── true_vs_predicted_labels.png | True vs predicted labels plot | Link |
| └── data/ | EEG data files for all subjects — MATLAB .mat files | Data |
| ├── s1.mat | EEG data for Subject 1 — MATLAB .mat file | s1 |
| ├── s2.mat | EEG data for Subject 2 — MATLAB .mat file | s2 |
| ├── s3.mat | EEG data for Subject 3 — MATLAB .mat file | s3 |
| ├── s4.mat | EEG data for Subject 4 — MATLAB .mat file | s4 |
| ├── s5.mat | EEG data for Subject 5 — MATLAB .mat file | s5 |
| ├── s6.mat | EEG data for Subject 6 — MATLAB .mat file | s6 |
| ├── s7.mat | EEG data for Subject 7 — MATLAB .mat file | s7 |
| ├── s8.mat | EEG data for Subject 8 — MATLAB .mat file | s8 |
| ├── s9.mat | EEG data for Subject 9 — MATLAB .mat file | s9 |
| └── s10.mat | EEG data for Subject 10 — MATLAB .mat file | s10 |
As an MSc candidate in Biochemistry & Molecular Biology, specializing in Molecular Cancer Biology, this summer school offered hands-on experience in neurotechnology and EEG-based brain research, enhancing my ability to integrate computational and experimental approaches. Key outcomes include:
- Practical EEG data collection, preprocessing, and analysis.
- Deepened understanding of signal processing and feature extraction techniques.
- Application of machine learning models to biomedical datasets.
- Experience in project-based teamwork, documentation, and scientific reporting.
- Awareness of ethical considerations in human-subject neurotechnology research.
This training significantly strengthened my computational and experimental skills, providing a bridge between molecular biology and cutting-edge brain-computer interface technologies, relevant for neuroscience and neuro-oncology research.
- EEG experimental design and data acquisition
- Signal preprocessing and artifact removal(MATLAB with EEGLAB toolbox)
- Feature extraction (temporal, spectral, time-frequency domains)
- Machine learning applied to EEG datasets (CCA)
- Jupyter Notebook workflow development for reproducible research
- Data visualization and analysis for neurotechnology projects
- Scientific project reporting and presentation
- Collaborative teamwork in research projects
Mohamed H. Hussein — MSc Candidate, Biochemistry & Molecular Biology, Ain Shams University
- Participated in all summer school sessions, tutorials, and workshops on neurotechnology and EEG-based research.
- Performed EEG data acquisition, signal preprocessing, feature extraction, and machine learning analysis in the project lab.
- Co-developed the project presentation with a teammate, enhanced the initial Jupyter Notebook based on workshop materials, and jointly presented the results.
- Re-engineered independently the entire Jupyter Notebook code and substantially improved it to produce a fully reproducible workflow, including enhanced processing steps,figures, clearer documentation, and a clean, structured analytical pipeline suitable for public sharing and reuse.
- Prepared independently detailed Event Notes for all five sessions and developed the complete Analysis Project Report.
- Created independently the entire GitHub folder, including all Jupyter Notebooks, presentation slides, Event Notes, and the project Analysis Report, with a well-organized folder structure ensuring clarity and easy navigation.
- Gained hands-on experience integrating molecular biology, computational analysis, and neurotechnology, with strong relevance to neuroscience and neuro-oncology applications.
This folder contains summary, notes, project materials, and the reproducible Jupyter Notebook, and is part of the Research-Trainings-2024 repository.
Citation: Hussein, Mohamed H. (2025). Research Training 2024 [ Summary, Notes,and Project]. GitHub repository: https://github.com/Mohamed-H-Hussein/Research-Trainings-2024
Usage: This folder serves as a personal learning and skill-tracking resource for:
- Reviewing EEG and neurotechnology concepts
- Practicing data analysis workflows learned during the summer school
- Supporting professional development and educational documentation
- Personal reference for ongoing and future neurotechnology projects
All materials authored by me are available for educational and non-commercial use, provided proper citation is included. Any reused or modified material should clearly acknowledge the original author. Summer School-provided materials remain under the copyright and license of the original organizers.
This folder is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). Full license: https://creativecommons.org/licenses/by-nc/4.0/legalcode
© 2025 Mohamed H. Hussein