Biophysics is an integral part of biology, even though it is not a standard part of the K-12 curriculum. That is why the Biophysical Society provides additional information and lesson plans. Teaching biophysics allows for a large number of demonstration experiments and laboratory work for students to complete in lessons.
We would like to present a simple approach to support teaching challenging curriculum dealing with neural tissue in hands-on experiments. Unlike electrocardiography (ECG) and electromyography (EMG) measurements, where voltages measured on the human body are in the order of millivolts, electroencephalography (EEG) is measured on the head and voltages are in the order of microvolts. This makes ECG and EMG measurements a much bigger challenge. Although the construction of an EEG amplifier is not impossible, it is challenging in a typical school environment. Therefore, experiments with a commercially available, but relatively cheap, EEG headset are described below.
The Neurosky MindWave Mobile headset was chosen because of the low price and Bluetooth capability. There are many instructions for interfacing with the headset, for example using Arduino. For our purposes, we used a personal computer. The headset can be paired with a computer over Bluetooth and a program called ThinkGear Connector allows other software to receive data from the headset. Of course, the measured data are not comparable to commercial medical EEG devices because only one differential electrode is used instead of the many used in medical devices. However, even this one measured curve is sufficient for demonstration in the classroom and for simple tasks that students can perform by themselves.
This particular headset have to be paired with the computer via BlueTooth (technically speaking, headset acts as a virtual serial port while data from the serial port can be handled very easily in software). We learned that if there is a problem, fresh pair of batteries efficiently solves the problem.
For the lecture demonstrations and motivation purposes, MindWave Mobile Starter Kit bundled with the headset is absolutely sufficient. MindWave Mobile Core application shows not only the raw data stream in form of graph (right upper part of the picture) as well as Fast Fourier transformation (FFT) spectrum (color bars), which can be used to estimate the spectrum components of the EEG data.
Students can see how their brain activity affects the displayed values of attention and the shape of the measured EEG curve, for example, while they try to solve a mathematical problem. The program window can be presented to the classroom via a data projector and EEG curve changes representing changes in voltage, measured on the volunteer's head, can be easily seen.
Because there are more frequency components than one, left part of the appliaction window shows most prevalent EEG rythms. Bottom-right part shows calculated values of "Attenttion" and "Meditation". These values are calculated according to the FTT spectrum of the EEG data and are calculated on the headset microchip, the MindWave Mobile Core application gets them from the headset to display it on the screen.
For the junior college level, EEG data analysis can be done. For this exercise, raw data have to be obtained from the headset. There are several methods, for example, if the matlab software is available, matlab script can be used.
To further ease the data collection, we used small appliacation called ThinkGear Connector. This app runs as a background process on the computer and can direct headset data from the serial port to an open network socket. This network socket can be for example written in the Processing programming language, which is a PC counterpart of the Arduino programming language called Wiring. Processing is very easy programming language taught in a beginning high school or university computer science classes.
We have modified the Processing sketch by Yang, by adding the capability of saving the raw data to the text file. The final sketch (mindwave.pde) can be downloaded here. Data can be then visualised and analyzed in any spreadsheet editor (for example Microsfot Excel).
These data correspond to the measured electrical voltage on the head of the pupil, i.e., they are a record of the electrical activity of the brain. Even if the Fast Fourier transformation is not known to students, straightforward estimation of prevalent EEG rhythms can be made. The most significant minima (circled in the left subfigure) are approximately 100 to 200 milliseconds apart, which means that the most significant frequencies will roughly be in the 5 to 10 Hz range. This simple exercise can be done with a ruler and printed sheet of data and verified by computer calculations made by the teacher (right panel).
Here you can download lab handouts and customize it for your needs. In the left panel, there is information about nerve tissue that students can fill out during a lecture. In the right panel, there is information about EEGs. Several EEG curves that students can analyze independently (normal EEG, deep sleep, and EEG during an epileptic seizure) can be downloaded here.
We believe that laboratory exercises like this are not beneficial only for future medical doctors, but for all students that study biology. Biophysical measurements, or laboratory exercises involving medical devices, with first-hand experience, are especially popular because the students can be direct participants. If these laboratory exercises strengthen students’ operational data processing skills, they are doubly useful. Although it may seem that these exercises are only usable at the college level, they can act as substantial motivational factors at the secondary school level. The cost of the EEG headset is within the means of most public schools.
Source code: File mindwave.pde
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