Application to biomedical signals - Practical Biomedical Signal Analysis Using MATLAB - page 164

surgery will not seriously disturb sensory, motor, or cognitive functions. iEEG pro-
vides unprecedented opportunity for studying indices of cortical activation, since it
is characterized by high temporal resolution and mesoscopic spatial resolution that is
intermediate between the macroscopic scale of EEG/MEG and multiunit recording
of neuronal activity.
iEEG allows for investigation of high-gamma activity, which is hard to observe us-
ing scalp electrodes. Causal interaction between signals in high-gamma range (above
60 Hz) for the word repeating task were studied by [Korzeniewska et al., 2008].
The authors introduced a new measure—short-time direct directed transfer func-
tion (SdDTF), which combined the benefits of directionality, directedness, and short-
time windowing. This function appeared to be an effective tool for analyzing non-
stationary signals such as EEG accompanying cognitive processes. The performance
of the function was tested by means of simulations, which demonstrated that the
SdDTF properly estimates directions, spectral content, intensity, direct causal inter-
actions between signals, and their time evolution. To evaluate event-related changes
in SdDTF, that is, event-related causality (ERC), a new statistical methodology was
developed to compare prestimulus and poststimulus SdDTF values. This procedure
is described in Sect. 4.1.7.3.7.
In order to quantitatively describe the transmissions, ERC for high gamma activ-
ity was integrated in the 82-100 Hz range, which was empirically derived based on
the mean ERC over all time points and all pairs of analyzed channels. Figure 4.24
shows the magnitudes of the interaction in the form of arrows of different widths.
The most prominent identified connections involved: in the first phase (listening)
flows from the auditory associative cortex to mouth/tongue motor cortex, and in the
second phase (repeating of the word) propagation from the Brocas area (responsible
for speech) to mouth/tongue motor cortex.
One of the problems important for neuroscience is the relation between the spike
trains and local field potentials. For the spike train evaluation the methods developed
in the field of point processes analysis are customarily applied [Brown et al., 2004].
Nevertheless there is a possibility of also using the broad repertoire of stochastic
continuous signal analysis methods, described in this topic, to spike trains.
In the approach proposed by [Kocsis and Kaminski, 2006] the spike trains were
processed in the following way: the spikes were low-pass filteredbyanorder1But-
terworth filter with cutoff frequency at 10% of Nyquist frequency (the filtering pro-
cedure was applied as zero phase filter; Sect. 2.1); then 10% of stochastic noise
uncorrelated with the signal was added in order to make the spike train better match
the stochastic character of the AR model. The procedure is illustrated in Figure 4.25.
The described approach was used in the experiment where LFP was recorded from
hippocampus and spike trains from the supramammilliary nucleus (SUM) of a rat
with the aim of finding the dynamic coupling between the structures in a situation
when the sensory stimulus was applied. The MVAR model was fitted to the spike
signal from SUM (approximated in the above described way) and the hippocam-
pal LFP; then the SDTF functions were estimated. The temporal dynamics of the
direction of influence revealed sharp reverses in the direction of the theta drive in
association with sensory-elicited theta rhythm. It was found that in this situation the