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Soni
cation of EEG Rhythms (Hinterberger and Baier
2004
). Spurred on by
research indicating the superiority of audio over visual feedback in a system with
multiple inputs (Fitch and Kramer
1994
), POSER applied musical mappings to
assist real-time analysis of EEG information. In initial implementations of POSER,
features of multiple brainwave rhythms were mapped to MIDI instruments and
presented to users. Continuous sounds were modulated in pitch and volume
according to changes within the bandwidth of a corresponding rhythm. Reports
showed that users were able to evoke control over individual EEG rhythms, as
successfully as 85 % during trials, using musical notes as real-time feedback for
simultaneous EEG data. This approach is later adopted in a system that screens
EEG for dynamic characteristics (Baier et al.
2007
), such as those prominent in
diseases including epilepsy and Alzheimer
'
s (Jeong
2002
). Here, events of interest
within EEG are mapped to digital synthesis parameters in Csound music software
(Boulanger
2000
), to aid in the distinction between normal and abnormal rhythms
in patients. By connecting expected EEG artefacts to synthesis features such as
amplitude modulation and harmonic content, a sonic real-time interpretation of
meaningful data is available. In another system, the use of sound localisation via an
array of speakers is used to re
ect the horizontal location, across the scalp, of the
current activity. Further work into these soni
cation techniques also addressed
interaction and user acceptance issues (de Campo et al.
2007
).
10.6
Computer Music and the Brain
The mappings in early experiments with music and brainwaves were built into the
hardware that was used. They were pre-determined by the equipment available,
they were
cult to change or undo. BioMuse, a hardware and
software system developed by Benjamin Knapp and Hugh Lusted in the 1990s,
introduced a major departure from this, with the use of real-time digital computing
to process EEG data (Knapp and Lusted
1990
).
BioMuse provided a portable kit for digitally processing bio-signals, but what
was ground breaking was that it was able to convert these signals into MIDI data.
Thus, creating a MIDI controller based on bodily responses, BioMuse also mea-
sured eye movements, muscle movements and sound from a microphone input.
This use of the MIDI protocol allowed for an EEG signal to be mapped to the input
of MIDI-enabled equipment, such as a synthesiser, a drum machine or a sequencer.
Furthermore,
fixed and they were dif
fine-tuning of input data. An input
threshold switch and a channel sensitivity control meant that the system could be
calibrated for different users and different applications. Adjusting the threshold
allowed for amplitudes over a speci
the technology allowed for
ed MIDI command,
and increasing the channel sensitivity increased the number of MIDI values in a
corresponding range. A demonstration of BioMuse presented at the New Music
Seminar 1990 in New York City showcased this method of mapping multiple bio-
signals to MIDI parameters.
ed level to trigger a speci
