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In-Depth Information
Fig. 1.2
A typical example
of Hjorth analysis of an EEG
signal
from the given examples. Then, it sequences those blocks in a domino-like manner
based on the deduced rules (Miranda and Boskamp
2005
).
Every time the system is about to produce a measure of music, it checks the
power spectrum of the EEG at that moment and triggers the generative music
instructions that are associated with the most prominent EEG rhythm in the signal.
These associations are arbitrary and can be modi
ed at will, which makes the
system very
flexible. The system is initialized with a reference tempo (e.g. 120
beats per minute), which is constantly modulated by Hjorth
'
is measurement of
complexity.
The EEG can in
ned
way. We implemented a statistical predictor, which uses the deducted rules to
generate short musical phrases with a beginning and an end that also allows for real-
time steering with EEG information. The system generates musical sequences by
de
uence the algorithm that generates the music in a well-de
and
methods of generating similarity relationships or contrast relationships between
elements. Consider the following example in LISP-like notation:
ning top-level structures of sequences
—
referred below as sentences
—
S -> (INC BAR BAR BAR BAR BAR HALF-CADENCE 8BAR-COPY)
From this top-level, the system retrieves rules for selecting a valid musical
building block for each symbol (INC, BAR, etc.) and a rule for incorporating the
EEG information in the generative process. For example:
INC -> ((EQUAL
'
MEASURE 1)
'
COMPOSER
EEG-SET-COMPOSER))
BAR -> ((CLOSE
(EQUAL
'
PITCH
'
PREV-PITCH-LEADING)
(CLOSE
'
PITCH-CLASS
PREV-PITCH-CLASS-LEADING)
(EQUAL
'
COMPOSER
EEG-SET-COMPOSER))
'
