Goal: How to measure the brain activity using scalp recordings?

• Chapter 3 on Electroencephalography in: John T. Cacioppo, Louis G. Tassinary, Gary G. Berntson - Handbook of Psychophysiology (Cambridge University Press, 2007). Most important parts:
  • Physiological basis of the EEG (pp. 57-59)
  • Normative EEG activity (pp. 59-61)
  • Data acquisition and signal analysis (pp. 61-65)
  • Quantitative analyses: Spectral analyses, Time-frequency analyses (pp. 65-67)
• Emily S. Kappenman, Steven J. Luck - ERP Components: The Ups and Downs of Brainwave Recordings (The Oxford Handbook of Event-Related Potential Components, 2012) (read up to “ERP Peaks ≠ ERP Components”, including this section; i.e., up to page 12)

1. Q&A Session:
   1. What is actually measured during an EEG measurement? Are we directly reading the subject's thoughts?
   2. Are you familiar with the purpose of the 10-20 system?
   3. What are the event-related potentials (ERPs)?
   4. Compare the typical EEG bands (delta, theta, alpha, beta, gamma, mu)
   5. What kind of artifacts are most commonly observed in the EEG signal?
2. Measurement time:
   1. Follow the Devices 101 lab to connect the biosignalsplux with the PC
   2. Use the EEG Datasheet and EEG User Manual to properly record the EEG signal
   3. Tasks:
      1. The EEG signal is very sensitive to distortions – especially eye-related interference. Check:
         • what kind of disturbances are caused by eye blinking?
         • if there is a difference between the signal recorded with the eyes open and with the eyes closed (here you may need to plot the power spectrum to see the differences)?
         • what kind of disturbances are caused by moving the head?
      2. We can break down the EEG signal into different component frequencies (alpha, beta, gamma, delta) - some of which are associated with rest and others with activity. Record about 3 minutes of signal when you are relaxed and about 3 minutes of signal during intense activity (e.g., playing chess online; it is important to move as little as possible) and see if you can see the differences!
         • Note: here we need to do a frequency analysis - it is introduced in the advanced part of the notebook + it is also placed in the eeg_extract_alphaband notebook from biosignalsplux
   4. At the end of class:
      1. clean the equipment with disinfectant wipes
      2. throw away the disposable electrodes (or keep them as souvenirs )
      3. pack all items in bags
      4. make sure all items are in the case:
         • biosignalsplux hub
         • bluetooth dongle
         • 4 sensors (each has a sticker with the name on it)
         • reference electrode (single, no stickers)
         • power supply
      5. give the case to the teacher
3. Practice session:
   1. Today's lab is placed in one Jupyter Notebook: Electroencephalography (EEG)
   2. Note: Loading your own signal into mne.tools can be a bit tricky. Therefore, if you want to perform simple analyses on your own signal recorded with biosignalsplux, do the eeg_extract_alphaband notebook from biosignalsplux instead of the regular notebook
4. Advanced practice session:
   1. If you want to tackle additional topics, do the optional Advanced section in the notebook. It will give you the opportunity to perform the basic frequency analysis of the EEG signal (and deal with the magic alpha, beta and theta waves!).

• T.J. Sejnowski, P.S. Churchland & J.A. Movshon - 2014 - Putting big data to good use in neuroscience – nice comparison of methods' temporal and spatial resolution
• NeurotechX community (incl. rich discussions on dedicated Slack)
• Neurotech Microcredential Program (courses online)

• Chapter 3 on Electroencephalography and Chapter 4 on Event-Related Brain Potentials in: John T. Cacioppo, Louis G. Tassinary, Gary G. Berntson - Handbook of Psychophysiology (Cambridge University Press, 2016) – great handbook if you want to know more about all the physiology behind analysed signals (3rd edition available online via EBSCO)
• Steven J. Luck - An Introduction to the Event-Related Potential Technique (Bradford Book, 2014) – the best handbook on EEG signal analysis

• Publication guidelines and recommendations for studies using electroencephalography and magnetoencephalography – How to perform EEG/MEG studies properly and how to describe them in the publications (to facilitate studies replication)
• A Reproducible MEG/EEG Group Study With the MNE Software: Recommendations, Quality Assessments, and Good Practices

• Removing electroencephalographic artifacts by blind source separation – how to clean the EEG data properly? one of the most cited papers in Psychophysiology journal
• EEGEdu – live EEG tutorial for Muse Headset (or mock data, if you don't have the device)

• Review of brain encoding and decoding mechanisms for EEG-based brain–computer interface – nice review of Brain-Computer Interfaces' methods
• BCI 101 and Neuroscience 101 – open courses developed by NeuroTechX

• mne.tools (the best Python package for brain-related data analysis)

• NeuroKit2 (toolbox for BVP, ECG, EDA, EEG, EMG, EOG, Respiration; it has a physiological signal generator)
• BioSPPy (toolbox for BVP, ECG, EDA, EEG, EMG, Respiration)