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neural marker that their comprehension of language is still affected (Kotz &
Friederici,
brain
2003; Meechan et al., 2021). Moreover, the N400 disruptions show
-related evidence that semantic language processing is also affected in Broca’s
aphasia. Wernicke’s aphasia is less studied with EEG but it has been observed that
these patients ge nerate inconsistent N400 responses (Kotz & Friederici, 2003), as
be expected due to the clearly impaired language comprehension. Using an
would
additional ERP component, the Phonological Mapping Negativity to check auditory
processing, Robson et al. (
2017) support that Wernicke’s aphasia patients
already
have phonological processing deficits, which likely contribute to comprehension
deficits and the related N400 alteration.
Not only can EEG contribute to neuropsychology research, but frameworks
developed in neuropsychology can also guide EEG basic research. As an example,
Wischnewski et al. (
relations
hip between the ongoing EEG beta and mu oscillations to corticospinal
2022) relied on the concept of double dissociation to clarify the
excitability. In the experiment, they applied transcranial magnetic stimulation on the
motor cortex during the positive or negative phase of the ongoing mu or beta
oscillations. They looked at the effect it had on the motor evoked potential as an
index of corticospinal excitability. Results showed greater excitability during the
peak and the falling phase of beta, and less during the trough and rising phase,
suggesting an excitatory role. The opposite was observed for mu: greater excitability
during the trough and rising phase, and less during the peak and the falling phase,
supporting an inhibitory role. The double dissociation between ongoing beta and mu
contributes to the understanding of these rhythms and can help the fine-tuning of
transcranial magnetic stimulation to reduce response variability for research and
clinical purposes (Wischnewski et al.,
2022).
The previous examples show how clinical observations guided by neuropsychology
and EEG research inform each other, with particular attention to the logic of
dissociation of functions. Though this approach has been fruitful in enhancing our
understanding of the brain, the limitations in lesion studies and the logic of dissociation must also be kept in mind (Vaidya et al., 2019). For instance, the lesions in
ts rarely affect a single region of the brain. There is also damage to adjacent
patien
regions and generalized brain effects related to the injury (e.g., inflammation).
Lesions involving a studied region also vary in type and severity between patients.
Such conditions make it harder to isolate the effects of the lesion on a single
cognitive function and attribute it to a particular brain region. Observations of
many patients with overlapping lesions are required to compensate for this disadvantage. Another important aspect is that certain cognitive functions may emerge
from distributed networks, rather than singular locat ions in the brain (Ramminger
et al.,
2023; Vaidya et al., 2019).
Limitations
are not an impediment for research to continue evolving. In fact, it is
this type of limitation that fuels the exchange between methodological perspectives,
and the use of different techniques in neuroscience. From the prosopagnosia examples above, functional magnetic resonance imaging helped in the detection of a
neural network related to face recognition beyond the fusiform gyrus, while the
N170 ERP component helps to clarify how damage to the different regions affects
23 EEG in Basic Science and Academic Research 307
the perception or recognition of a face. Furthermore, technical and methodological
advances within EEG help to address open questions such as the proposal that
distributed networks generate a cognitive function, which can be addressed through
connectivity techniques in EEG. As an example, Rutar Gorišek et al. (
connect
broader working memory network that gets disrupted during injury, and this could
explain why these patients also show some language comprehension deficits.
ivity measures in Broca’s patients to suggest that Broca’s area is part of a
2016) used
23.1.2 Mental Chronometry and EEG
Mental chronometry is a discipline that evolved with the aim of timing mental
activity, and disentangling what cognitive processing stages occur behind overt
behavior. As such, it has been an important theoretical and methodological source
for EEG in cognitive research. At the same time, the EEG’s precise time resolution
has made it an ideal ally for mental chronometry as it enables the timing of neural
markers that happen before or without overt behavior.
Mental
exist whose final outputs manifest in behavior, and an alteration of one of those
cognitive proces ses will modify behavior (Linden, 2007; Meyer et al., 1988). As a
result, it is possible to manipulate task conditions to target a specific cognitive
process such as attention, working memory, or decision making. Inferences on the
target cognitive process are made based on alterations in reaction time, in accuracy,
or speed-accuracy tradeoffs (Meyer et al., 1988).
of the pioneers in mental chronometry, Franciscus Donders, in the late nineteenth
century, who used reaction times to infer cognitive processes (Vidal et al., 2011). In
his
comparing the reaction times between three tasks. In task A “simple reaction
time”, participants had to press a button as quickly as possible following a stimulus.
In task B “choice reaction time”, two different stimuli were presented in alternation and participants had to push the correct button that corresponded to the target
stimulus. Task B added the processes of stimulus discrimination and response
selection. In task C “go-no-go”, one of two stimuli could be presented but participants only had to respond to one type of stimulus and refrain the response to the
other. Task C then required stimulus discrimination but not response selection.
Donders reasoned that by subtracting the reaction times from these tasks, it would
be possible to determine the timing of the particular cognitive operations that each
task requires (Meyer et al.,
respon
inate the common process of target discrimination, and reveal the timing needed for
response selection. Similarly C–A would eliminate the common process of response
execution, and reveal the timing for stimulus discrimination. With EEG, it is possible
to check the timing of ERPs during reaction time experiments to complement the
subtraction method. In this way, Vidal et al. (
chronometry relies on the assumption that separable cognitive processes
A useful example comes from one
experiments, he devised a subtraction method to time mental processes by
1988). As task B required target discrimination and
se selection, but task C only needed target discrimination, B–C would elim-
2011) observed that Donders’ choice
308 F. Cross Villasana
Fig. 23.1 Example of the subtraction method applied to alpha (8–12 Hz) power during an eyes-
closed and an eyes-open condition. On top, the spectrum plot shows a clear power reduction effect
after eye opening. When mapping alpha over the scalp, anterior alpha levels common to both
conditions occlude the alpha reduction localization. The contrast is created by subtracting the
spectra of the eyes-closed from the eyes-open condition. This eliminates the common anterior
activity and isolates the alpha power reduction effect to the occipital region
and Go/No-go tasks showed differences in brain motor activity and response execution timing in the response process that they have in common. This shows that
although Donders’ method is helpful, the assumption that additional processes can
be “purely inserted” into a task only partially holds (Vidal et al.,
2011).
Besides its use in reaction time, the subtraction logic has also been applied in
EEG and neuroimaging. The brain activity levels recorded under two conditions are
subtracted from each other to eliminate common neural processes, and isolate a
specific neural signal (Luck, 2014). A simple example can be observed in Fig. 23.1,
the power levels in different bands are compared between an eyes-closed and
where
23 EEG in Basic Science and Academic Research 309
an eyes-open condition. The plot of channel Oz shows the classic reduction of power
in the alpha band (8–12 Hz) after eye opening. When plotting alpha levels on the
scalp, a difference can be spotted in the occipital region, but it is perhaps not as clear,
and comes along with common activity in anterior regions. By subtracting eyesclosed from eyes-open activity to create a contrast, the neural activity common to
both conditions is largely eliminated, revealing the alpha r
eduction more clearly in
the occipital region.
Like in mental chronometry, the subtraction method has also received criticism in
the EEG and imaging domains. It is argued that this method may miss relevant
neural modulations produce d by the experimental conditions, which are better
captured through more advanced methods (Alexander et al.,
2015; Vidal et al.,
2011). Nonetheless, due to its availabil ity and ease for exploring neural signals,
subtraction method continues to be widely used by researchers.
the
In mental chronometry, the Donders’ subtraction technique requires that the
cognit
ive processes can be inserted or removed by the experimenter, but this is not
always possible. Considering this, the “additive factors” method was developed by
Saul Sternberg (Linden ,
a
task, one identifies factors that may affect the duration of particular processing
2007). Instead of adding or removing cognitive processes to
stages. For example, in a reaction time experiment the modification of the factor
“stimulus visual clarity” can be used to affect a stage of stimulus perception; and the
modification of the factor “frequency of the target stimulus” to affect a stage of
response selection. If altering two factors leads to additive effects on reaction times,
it can be proposed that at least two separate processing stages were affected. In this
way, by manipulating factors one can make inferences about the kinds of processing
stages behind overt behavior (Meyer et al.,
fruitful in the design of EEG experiments, as it allows for disentangling various
been
1988). Additive factors methodology has
processing stages within a single experimental task. As an example, in the quest to
clarify the meaning of ERP components, Smulders et al. (
react
ion time task where either of the factors: clarity of the stimulus or complexity of
1995) used a 2-choice
the response was varied. The task was paired with assessment of the P300 ERP
component, known to respond to stimulus evaluation, and the LRP (lateralized
readiness potential) component, related to the response preparation stage. Degraded
stimulus quality caused delays in both components following the stimulus presentation. Response complexity did not delay the P300, as predicted, however it also did
not cause delays in LRP. Response complexity delayed the time between both
components and response execution. These results support the idea that the P300
is not involved in response processes. They further suggest that while the LRP
precedes response programming, it may not be involved in the previous response
selection, since the onset of the LRP was postponed by delays in the previous
stimulus evaluation, and the distance from the LRP to the overt response was
increased by response complexity. With these results, the authors also support that
information transmission in the brain follows consecutive discrete stages during this
task (Smulders et al.,
Both Donders
1995).
’ subtraction and Sternberg’s additive factors methods rely on the
assumption that cognitive processes are discrete and follow each other serially,
310 F. Cross Villasana
starting only when the previous stage has concluded (Linden, 2007). These frameworks do not consider the possibility of different processes running in parallel, or
that a process could already start with partial information fed forward from the
previous stage. Further models have been developed to address these questions. As
an example, the cascade model (McClelland,
start after reaching an input threshold from a previous stage, implying that the
kick-
1979) posits that a processing stage can
flow of information is continuous. In the stimulus to response mapping, the Eriksen
and Schultz continuous flow model (Coles et al.,
partiall
y processed visual information is available, multiple competing response
1985) proposes that as soon as
processes start in parallel, which are refined with more complete visual information.
In EEG, response monitoring processes are likely candidates that reflect continuous
and parallel information processing. Going back to choice reaction time, it has been
reported that when errors are committed, the ERP component “Error Related Negativity” (ERN; Gehring et a
respon
se. However, a smaller ERN appears during partial errors where a participant
l., 20
b
ecomes prominent following the incorrect
18)
rectifies just before pressing the wrong button, as reflected by hand muscle activity.
Moreover, an even smaller ERN is also present during correct responses (Vidal et al.,
2000). This suggests that ERN may reflect a response verification process that starts
dy before the response is emitted (Vidal et al., 2000). With these characteristics,
alrea
it
has been proposed that ERN modulations reflect the model of continuous flow of
information (Ullsperger et al.,
2014). Although the significance of the
ERN is still
being refined, it is a useful tool for researching the characteristics of cognitive
processes.
The various cognitive models in the field involve questions such as whether
information processing between regions of the brain is seri al or parallel, discrete or
continuous. Her e EEG can play a role in clarifying these open quest ions. At the same
time EEG research profits extensively from the framework of mental chronometry.
Nonetheless, one must be aware of limitations when combining EEG and mental
chronometry. In the case of ERPs, as exemplified above, the putative processes that
various ERP components represent do not always coincide with the processing
stages proposed in mental chronometric models (Linden,
2007). Components can
overlap with each other, so it is not always easy to define their timing (Linden, 2007).
Some
ERP components seem to result from diverse neural sources from various
areas in the brain, possibly representing different cognitive processes. An example is
the P300, which is estimated to have multiple brain sources (Huang et al.,
is also known to be modulated under different contexts such as sudden
P300
2015).
unexpected stimuli, or frequent but more complex stimuli (Huang et al., 2015).
EEG
measurements are also mostly confined to the scalp, while some cognitive
processes are related to deeper brain areas. Conside ring the capabilities and limitations of EEG measurements, conducting a conscientious literature review of the EEG
signals and mental chronometric models to use, and a careful experimental design
represent the best way to implement a successful study.
23 EEG in Basic Science and Academic Research 311
23.2 Research on the EEG Sig nals
Knowing what EEG signals represent is paramount for their use in research. In the
examples from the previous section, EEG was mostly implemented to help answer
questions about neurocognitive processes. But to achieve a more thorough understanding of the EEG itself, specific studies are required to disentangle the meaning of
its various signals. With this aim, multiple complementary approaches are used to
provide context for the observed modulations in the EEG: besides behavioral
manipulations and mental chronometric models, joint measurements with other
techniques like transcranial magnetic stimulation (TMS) or functional magnetic
resonance imaging (fMRI) are implemented. A broader electrophysiological perspective in human, animal, and in-vitro studies has also helped to explain the
neuronal origins of the EEG, and the physiological states that underlie different
EEG rhythms.
A good example is research on the meaning of alpha band oscillations. From its
rst observation, alpha was seen in contexts that implied down-regulation of cortical
fi
activity such as relaxing or closing the eyes. Traditional cognitive experiments show
that ongoing alpha decreases transiently during the presentation of stimuli, indicating activation (Klimesch et al.,
enhances
tion (Hummel et al., 2002). Using TMS, the brain’s motor area shows a decreased
respon
magne
erate a smaller response than during the lower alpha power (Taylor & Thut, 2012). In
the
during higher alpha power (Taylor & Thut, 2012). When pairing EEG and fMRI, it
can
(Murta et al., 2015). On a physiological level, one mechanism observed for alpha
rhyth
project towards the cortex during periods of inactivity, particularly in visual and
motor systems, giving shape to oscillations in the alpha range (Sterman, 1996).
Altoget
where it plays an inhibitory role over the cortex. It is further proposed that this plays
a role in pacing and coordinating cognitive systems (Beste et al.,
Mazahe
different aspects of the brain or in other academic areas. For instance, based on the
inhibitory properties of alpha rhythms, it has been proposed that the motor and visual
systems exert transient inhibition effects on each other, seen as an enhancement in
EEG alpha levels over the inactive system when the other is activated (Pfurtscheller
& Lopes da Silva,
can
anesthesia (Hight et al.,
observed that as consciousness fades, the down-regulation of anterior brain regions
alpha-range rhythms over motor areas, supporting a role of active inhibi-
se when preceding alpha levels are higher (Sauseng et al., 2009), more over,
tic pulses deployed to visual areas during higher ongoing alpha power gen-
same way, visual stimuli are less likely to be perceived if they are presented
be observed that cortical activation levels are lower when alpha levels are higher
ms in animal models is an excitation-inhibition loop of thalamic neurons that
her, these studies at different levels paint a more complete picture of alpha,
ri,
2010; Klimesch et al., 2007).
Studying the properties of EEG facilitates its use for answering questions on
1999). An example of a practical application is that EEG features
be used as an adjunct measure to monitor the state of consciousness during
2007). Meanwhile, withholding a motor response
2023; Jensen &
2020).
For the alpha rhythm in particular, it has been
312 F. Cross Villasana
is reflected by increases in frontally coherent alpha amplitude (e.g., Purdon et al.,
2013). Importantly however, this is not a definitive marker and the phenomenon is
varia
ble during practice, so it must be complemented with other measures (Hight
et al., 2020).
The above examples serve to illustrate how deeper knowledge of particular EEG
signals can be used to inform other fields. Besides the alpha example above, similar
cases exist for other EEG signals, e.g., ERP components, phase measurements, and
raw signal monitoring, among others. This knowledge is applied in multiple fields of
research (e.g., social or developmental neuroscience) whose scope is too broad to
cover here. The remaining chapters in this section explain practical applications of
EEG in greater detail, such as clinical uses, mobile EEG, or brain-computer
interfaces.
23.3 Conclusions
EEG is an invaluable tool for basic and academic research of the human brain and
cognitive processes. Cognitive EEG research has traditionally relied on concepts and
methods from the broader scope of psychology and neuroscience, such as neuropsychology, psychophysics, or mental chronometry. These disciplines provide
frames of reference such as the idea of dissociable cognitive functions from neuropsychology, or experimental methods for disentangling covert cognitive processes
from mental chronometry. These notions continue to evolve as multidisciplinary
research progresses, for example, the idea of discrete and serial processing stages,
versus cascade processes. The EEG is also a subject of study in itself, with experiments conduct ed specifically to improve understanding of the signals it generates.
This endeavor is not only helped by cognitive methodologies, but also by interaction
with other techniques like TMS or fMRI and relies on knowledge derived from
neurophysiology and the physics of the brain.
Besides traditional methodologies, advances in engineering and data processing
have enabled more complex analysis of the EEG, as well as its a pplication in novel
fields such as using portable devices or brain-computer interfaces outside of laboratories. Together, these new developments and traditional methods complement each
other in the pursuit of a deeper comprehension of the brain.
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