The topic of converting the so-called EEG (Electroencephalography) waves into the MIDI (Musical Instrument Digital Interface) music files has been of great interest to scholars over the recent decades. This can be explained by the fact that music created out of the waves that the brain of every human being produces can be used not only for entertainment but basically for health care purposes. The idea of designing a system to convert the EEG waves into music has always been interesting to scholars, and the system design discussed in this paper seems to combine a cost-efficient approach with the best results possible.
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Digital signal processing
The system proposed for use in the current project is built based on the LORETA algorithm argued about by Filatriau et al. (2007, p. 2). In more detail, the system design includes three major stages, i. e. EEG collection, digital signal processing, and MIDI representation. Graphically, the system design progress can be represented as follows:
So, the discussed system design will enable the research team to collect the EEG waves of the participants’ brains with the help of electrodes placed on their both hemispheres. Next, the data collected will be processed with the help of the LORETA algorithm (Filatriau et al., 2007, p. 2), and after this, the system will produce MIDI files based on analogies of EEG waves’ qualities with specific musical notes.
As seen from the above statement, the LORETA algorithm will play a crucial role in the operation of the system design (Filatriau et al., 2007, p. 2). This algorithm is based on four major criteria defined and calculated through the use of the following formulae. First, it is necessary to measure the potential of EEG wave occurrence, Φ. Second, the value of the sources producing those EEG waves, φ, is measured. The third and the fourth criteria that help in estimating the second point are the lead field matrix, G, and the rate of additional noise, ŋ (Filatriau et al., 2007, p. 2):
Φ = G φ + ŋ
At the same time, Ito et al. (2006, p. 1153) propose a slightly different formula that includes the role of mental change in sound stimulation, S, and the additional noise, N, for the calculation of Y, the time series data:
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In any case, both formulae require additional calculations, and Filatriau et al. (2007, p. 2) provide the rationale for them, arguing that the bayesian formalism fits the goal of defining the value of the sources producing those EEG waves from the above formula. So, the discussed design system will use the following formula to obtain the final data that would later be sent to the sound synthesis module (Filatriau et al., 2007, p. 2):
P(φ/Φ) = P(Φ/φ)P(φ) / P(Φ)
The data obtained through the above formula are ready for processing with the help of the LORETA algorithm that includes four stages:
- Sending the data to the sound synthesis module;
- Associating the brain zones with cognition, visualization, and movements;
- Creation of dipoles from the calculated data;
- Computing the dipoles and using them as features for creating the respective MIDI files (Filatriau et al., 2007, p. 2).
hus, the use of the LORETA algorithm is the basis on which the performance of the discussed system design is founded and the following scheme becomes possible:
- Frequency band
- Generative rules
- Signal complexity
- Tempo and dynamics
At this point, the system’s design performance is expected to follow this scheme and use the above discussed LORETA algorithm to enable the researchers to convert the EEG waves into MIDI music files.
Filatriau, J. et al. “From EEG signals to a world of sound and visual textures.” University of Plymouth, 2007, pp. 1 – 4. Print.
Ito, Shin-ichi et al. “The Proposal of the EEG Characteristics Extraction Method in Weighted Principal Frequency Components Using the RGA.” SICE-ICASE International Joint Conference 18.21 (2006): 1152 – 1155. Print.