Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_6027_Библиотеки_им_академика_М_И_Перельмана.pdf
25 From Brain Signals to Neuroadaptive Technology: BCIs for Human-Computer... 347
Krol, L. R., & Zander, T. O. (2022). Chapter 2—Defining neuroadaptive technology: the trouble
implicit human-computer interaction. In S. H. Fairclough & T. O. Zander (Eds.), Current
with
research in neuroadaptive technology (pp. 17–42). Academic.
science/article/pii/B9780128214138000075
Krol, L. R., Freytag, S.-C., & Zander, T. O. (2017). Meyendtris: A hands-free, multimodal tetris
Krol, L. R., Mousavi, M., et al. (2018a). Towards classifier visualisation in 3D source space. In
Krol, L. R., Andreessen, L. M., & Zander, T. O. (2018b). Passive brain–computer interfaces: A
Krol, L. R., Haselager, P., & Zander, T. O. (2020). Cognitive and affective probing: A tutorial and
Lawhern, V. J., et al. (2018). EEGNet: A compact convolutional neural network for EEG-based
Ligthart, S., et al. (2023). Minding rights: Mapping ethical and legal foundations of “neurorights.”
Lopes-Dias, C., Sburlea, A. I., & Müller-Putz, G. R. (2019). Online asynchronous decoding of
Lotte, F., et al. (2018). A review of classification algorithms for EEG-based brain-computer
Mehta, R. K., & Parasuraman, R. (2013). Neuroergonomics: A review of applications to physical
Mirkovic, B., et al. (2016). Target speaker detection with concealed EEG around the ear. Frontiers
Mueller-Putz, G., et al. (2008). Better than random: A closer look on BCI results. International
Mullen, T. R., et al. (2015). Real-time neuroimaging and cognitive monitoring using wearable dry
Norcia, A. M., et al. (2015). The steady-state visual evoked potential in vision research: A review.
Pan, Y., Zander, T. O., & Klug, M. (2024). Advancing passive BCIs: A feasibility study of two
Parasuraman, R., &
Pawlitzki, J.,
using eye tracking and passive BCI for intuitive neuroadaptive gaming. In Proceedings of
clone
the 19th ACM international conference on multimodal interaction. ICMI ’17. ICMI ’17:
INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION (pp. 433–437).
ACM.
https://dl.acm.org/doi/10.1145/3136755.3136805
2018
IEEE international conference on systems, man, and cybernetics (SMC) (pp. 71–76).
https://ieeexplore.ieee.org/abstract/document/8616017/
perspective
CRC Press.
brain%E2%80%93computer-interfaces-laurens-krol-lena-andreessen-thorsten-zander
review
012001. https://doi.org/10.1088/1741-2552/ab5bb5
brain-computer
2552/aace8c
Camb
S0963180123000245
error-related
https://doi.org/10.1038/s41598-019-54109-x
interfaces:
1741-2552/aab2f2
and
2013.00889
Neuroscience, 10, 349. https://doi.org/10.3389/fnins.2016.00349
in
Journal
EEG. IEEE Transactions on Biomedical Engineering, 62(11), 2553–2567.
1109/TBME.2015.2481482
Journal
temporal
EEG-based machine error detection. Frontiers in Neuroergonomics, 5, 1346791. https://doi.
org/10.3389/fnrgo.2024.1346791
University Press.
files/pawlitzki2021neuroergonomics-automationsurprise.pdf. Accessed 8 Oct 2025.
on increased interactivity. In Brain–computer interfaces handbook (pp. 69–86).
https://www.taylorfrancis.com/chapters/edit/10.1201/9781351231954-3/passive-
of active learning for neuroadaptive technology. Journal of Neural Engineering, 17(1),
interfaces. Journal of Neural Engineering, 15(5). https://doi.org/10.1088/1741-
ridge Quarterly of Healthcare Ethics: CQ, 1–21 .
potentials during the continuous control of a robot. Scientific Reports, 9(1), 17596.
A 10 year update. Journal of Neural Engineering, 15(3).
cognitive work. Frontiers in Human Neuroscience, 7, 889.
of Bioelectromagnetism, 10(1), 52–
of Vision, 15(6), 4. https://doi.org/10.1167/15.6.4
derivative features and effect size-based feature selection in continuous online
Rizzo, M. (Eds.). (2007). Neuroergonomics—The brain at work. Oxford
et al. (2021). Automation surprise in the neuroadaptive cockpit. https://lrkrol.com/
55.
https://www.sciencedirect.com/
https://doi.org/10.1017/
https://doi.org/10.1088/
https://doi.org/10.3389/fnhum.
https://doi.org/10.
348 M. Klug
et al.
Pfurtscheller, G., & Lopes da Silva, F. H. (1999). Event-related EEG/MEG synchronization and
desynchronization:
doi.org/10.1016/s1388-2457(99)00141-8
Pfurtscheller, G., et al. (2006). Mu rhythm (de)synchronization and EEG single-trial
of different motor imagery tasks. NeuroImage, 31(1), 153– 159.
neuroimage.2005.12.003
Polich, J. (2007). Updating P300: An integrative theory of P3a and P3b. Clinical Neurophysiology,
(10), 2128–2148. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2715154/
118
Pope, A. T., Bogart, E. H., & Bartolome, D. S. (1995). Biocybernetic system evaluates indices of
operator engagement in automated task. Biological Psychology, 40(1–2), 187–195.
org/10.1016/0301-0511(95)05116-3
Protzak, J., Ihme, K., & Zander, T. O. (2013). A passive brain-computer interface for supporting
gaze-based
Design methods, tools, and interaction techniques for eInclusion (pp. 662–671). Springer.
https://link.springer.com/chapter/10.1007/978-3-642-39188-0_71
Ramoser, H., Müller-Gerking, J., & Pfurtscheller, G. (2000). Optimal spatial filtering of single trial
Ratti, E., et al. (2017). Comparison of medical and consumer wireless EEG systems for use in
Roy, Y., et al. (2019). Deep learning-based electroencephalography analysis: A systematic review.
Samek, W., Kawanabe, M., & Müller, K.-R. (2014). Divergence-based framework for common
Schirrmeister, R. T., et al. (2017). Deep learning with convolutional neural networks for EEG
Schmidt, A. (2000). Implicit human computer interaction through context. Personal Technologies,
Shishkin, S., et al. (2016). Improving eye-brain-computer interface performance by using electro-
Vialatte, F.-B., et al. (2010). Steady-state visually evoked potentials: Focus on essential paradigms
Vortmann, L.-M., Ceh, S., & Putze, F. (2022). Multimodal EEG and eye tracking feature fusion
Willett, F. R., et al. (2023). A high-performance speech neuroprosthesis. Nature, 620(7976),
Wolpaw, J. R., & Wolpaw, E. W. (2012). Brain–computer interfaces: Something new under the
Wolpaw, J. R., et al. (2002). Brain–computer interfaces for communication and control. Clinical
Xavier Fidêncio, A., Klaes, C., & Iossifidis, I. (2022). Error-related potentials in reinforcement
during imagined hand movement. IEEE Transactions on Rehabilitation Engineering, 8(4),
EEG
441–446.
clinical
89/fnhum.2017.00398/full
Journal
spatial
10.1109/RBME.2013.2290621
decoding
1002/hbm.23730
4
encephalogram
https://cyberleninka.ru/article/n/improving-eye-brain-computer-interface-performance-byusing-electroencephalogram-frequency-components. Accessed 19 June 2022.
and
pneurobio.2009.11.005. Accessed 20 Nov 2025.
approaches
www.frontiersin.org/articles/10.3389/fcomp.2022.780580/full
1031
su
79712?redirectedFrom fulltext. Accessed 23 June 2023.
Neurophysiology
5702000573
learning-based
doi.org/10.3389/fnhum.2022.806517
https://doi.org/10.1109/86.895946
trials. Frontiers in Human Neuroscience, 11. https://www.frontiersin.org/articles/10.33
of Neural Engineering, 16(5), 051001. https://doi.org/10.1088/1741-2552/ab260c
patterns algorithms. IEEE Reviews in Biomedical Engineering, 7, 50– 72.
and visualization. Human Brain Mapping, 38(11), 5391–5420.
(2–3), 191–199.
future perspectives. Progress in Neurobiology, 90(4), 418–438. https://doi.org/10.1016/j.
–1036. https://doi.org/10.1038/s41586-023-06377-x
n. Oxford University Press. https://academic.oup.com/book/1700/chapter-abstract/1412
Basic principles. Clinical Neurophysiology, 110(11), 1842–1857.
https://doi.org/10.1016/j.
human-machine interaction. In Universal access in human-computer interaction.
https://doi.org/10.
https://doi.org/10.1007/bf01324126
frequency components. Bulletin of Russian State Medical University, 2, 36– 41.
for attention classification in hybrid BCIs. Frontiers in Computer Science, 4. https://
¼
, 113(6), 767–791. https://www.sciencedirect.com/science/article/pii/S138824
brain-machine interfaces. Frontiers in Human Neuroscience, 16, 806517. https://
https://
classification
https://doi.
https://doi.org/
25 From Brain Signals to Neuroadaptive Technology: BCIs for Human-Computer... 349
Yger, F., Berar, M., & Lotte, F. (2017). Riemannian approaches in brain-computer interfaces: A
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(10),
review.
1753–1762.
https://doi.org/10.1109/TNSRE.2016.2627016
Yuste, R., et al. (2017). Four ethical priorities for neurotechnologies and AI. Nature, 551(7679),
159
–163. https://doi.org/10.1038/551159a
Zander, T. O., & Jatzev, S. (2012). Context-aware brain-computer interfaces: Exploring the
information space of user, technical system and environment. Journal of Neural Engineering,
9(1), 016003.
https://doi.org/10.1088/1741-2560/9/1/016003
Zander, T. O., & Kothe, C. (2011). Towards passive brain-computer interfaces: Applying brain-
computer
Engineering, 8(2).
interface technology to human-machine systems in general. Journal of Neural
https://doi.org/10.1088/1741-2560/8/2/025005
Zander, T. O., et al. (2014). Towards BCI-based implicit control in human–computer interaction. In
S.
H. Fairclough & K. Gilleade (Eds.), Advances in physiological computing (pp. 67–90).
Springer.
https://doi.org/10.1007/978-1-4471-6392-3_4
Zander, T. O., et al. (2016). Neuroadaptive technology enables implicit cursor control based on
prefrontal cortex activity. Proceedings of the National Academy of Sciences, 113(52),
medial
14898–14903. http://www.pnas.org/lookup/doi/10.1073/pnas.1605155114
Zander, T. O., et al. (2017). Evaluation of a dry EEG system for application of passive brain-
computer
interfaces in autonomous driving. Frontiers in Human Neuroscience, 11, 78. https://
doi.org/10.3389/fnhum.2017.00078
Zhang, X., Krol, L. R., & Zander, T. O. (2018). Towards task-independent workload classification:
from binary to continuous classification. In Proceedings—2018 IEEE international
shifting
conference on systems, man, and cybernetics, SMC 2018 (pp. 556–561).
https://doi.org/10.
1109/SMC.2018.00104
Zhao, D.
G., et al. (2021). A passive BCI for monitoring the intentionality of the gaze-based moving
object selection. Journal of Neural Engineering, 18(2). https://doi.org/10.1088/1741-2552/
abda09
Chapter 26
EEG in Focal Epilepsy and Its Role
in the Management of Adult Patients
with Drug-Resistant Epilepsy
Boyuan Song, Umair J. Chaudhary, and Louis Lemieux
Abstract The principal role of EEG in focal epilepsy is to assist in diagnosis
classification of epilepsy subtype and identifying and localising epileptic
and
focus. Interictal and ictal EEG patterns both provide very useful information to
localise the epileptogenic zone in patients with drug-resis tant focal epilepsy, specially those undergoing presurgical evaluation. Scalp EEG, particularly prolonged
inpatient video-EEG monitoring, provides very valuable information in patients with
drug resistant epilepsy undergoing presurgical evaluation in most clinical situations,
whereas intracranial EEG is often needed in a small proportion of patients with drug
resistant focal epilepsy undergoing presurgical evaluation to refine the localisation of
epileptogenic zone and its relationship with the surrounding eloquent cortex.
Advances in AI-driven EEG interpretation hold promise for providing more objective, sensitive, and efficient clinical and research information in the future.
Keywords Focal epilepsy · Drug-resistant epilepsy · Scalp EEG · Intracranial EEG
26.1 Focal Epilepsy
The International League Against Epilepsy (ILAE) defines epilepsy as a brain
disorder that meets one of the following conditions: (1) At least two unprovoked
seizures >24 h apart; (2) One unprovoked (or reflex) seizure and a probability of
further seizures similar to the general recurrence risk (at least 60%) after two
unprovoked seizures, occurring over the next 10 years; (3) Diagnosis of an epilepsy
B. Song · L. Lemieux (*)
UCL Queen Square Institute of Neurology, University College London, London, UK
e-mail: boyuan.song.22@ucl.ac.uk; louis.lemieux@ucl.ac.uk
U. J. Chaudhary
UCL
Queen Square Institute of Neurology, University College London, London, UK
National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS
Foundation Trust, London, UK
e-mail:
umair.chaudhary@ucl.ac.uk
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026
Warbrick (ed.), The EEG Handbook,
T.
https://doi.org/10.1007/978-3-032-20450-9_26
351
352 B. Song et al.
syndrome (Fisher et al., 2014a). Since 2005, the ILAE uses the following definition
of a seizure: “A transient occurrence of signs and/or symptoms due to abnormal
excessive or synchronous neuron activity in the brain” (Fisher et al.,
re activity typically originates within a relatively confined region of the cerebral
seizu
2005). Focal
cortex and subsequently propagates to other brain areas through grey and white
matter pathways (Schevon et al., 2012). Focal seizures may originate in subcortical
2010)
structure
s (Berg et al.,
.
Seizures are the hallmark of epilepsy and are characterized by observable clinical
signs. Seizures are classified based on their clinical features, as illustrated in
Fig. 26.1. The main seizure classes include Focal, Generalized, and Unknown
(Beniczk
one
y et al., 2025). Focal-onset seizures originate within networks limited to
hemisphere, although they may subsequently propagate to broader cortical or
subcortical networks. In contrast, generalized-onset seizures engage bilateral brain
networks at seizure onset. The unknown category is used when available clinical or
electrophysiological information suggests a seizure onset, but the information is
Preserved consciousness seizure
Impaired consciousness seizure
Focal
Unknown
( whether focal
or generalized)
Seizure Onset
Generalized
Unclassified
Fig. 26.1 The 2025 updated ILAE classification of seizure types categorise them into further
subtypes based on consciousness i.e., preserved consciousness seizure or impaired consciousness
seizure. If a seizures starts as focal and then propagates to become a tonic clonic seizure it is
classified as focal to bilateral tonic clonic seizure. Under unknown, the subtypes are preserved
consciousness seizure, impaired consciousness seizure, and bilateral tonic-clonic seizure. Under
generalized, the subtypes are absence seizures, generalized tonic-clonic seizures, and other generalized seizures. (Adapted with permission from Beniczky et al.,
Focal-to-bilateral tonic-clonic seizure
Preserved consciousness seizure
Impaired consciousness seizure
Bilateral tonic-clonic seizure
Absence seizures
Generalized tonic-clonic seizure
Other generalized seizures
2025)
26 EEG in Focal Epilepsy and Its Role in the Management of Adult Patients... 353
insufficient to determine whether the onset is focal or generalized. A key change
from the 2017 ILAE classification is the addition of the “unclassified” category,
which is reserved for events confirmed by a clinician to be epileptic but lacking
sufficient information for any more specific classification. Electrographic seizures
are detected on electrophysiological recordings, typically without accompanying
clinical manifestations.
This chapter focuses specifically on adults with focal epilepsy, which the ILAE
defines as “originating within networks limited to one hemisphere; they may be
discretely localized or more widely distributed” (Berg et al., 2010).
In the early stages of epilepsy diagnosis, structural neuroimaging techniques such
magnetic resonance imaging (MRI) is employed to identify the underlying
as
aetiology (Cendes et al., 2016). Structural imaging can reveal an epileptoge nic lesion
in approximately 50% of patients with focal-onset seizures (Hakami et al., 2013),
althou
gh the yield increases with new developments in imaging, e.g. 7T MRI
(Klodowski et al., 2025).
Electroencephalography (EEG), a technique with high temporal resolution, is
widely
used to differentiate paroxysmal electrophysiological discharges, distinguish
focal from generalized seizures, and identify syndrome-specific patterns (Smith,
2005). Within the broader diagnostic framework of epilepsy, EEG contributes to
le aspects of evaluation, including the classification of epilepsy and seizure
multip
types, the diagnosis of status epilepticus, the localization of the epileptic focus, and
the assessment of seizure-recurrence risk (Misulis et al., 2022 ).
EEG records the summated electrical activity of populations of excitable neurons.
The
intrinsic electrical properties of these cells generate local electrical fields that can
be recorded using electrodes positioned at varying distances from the source. At
shorter distances, recordings capture local field potentials (LFPs), whereas at longer
distances such as recorded on the scalp, as in conventional scalp EEG, the recorded
activity represents the summation of the activity of extended and/or multiple cortical
generators (Lopes da Silva, 2009; Gloor, 1985). To effectively observe and monitor
activity, the recorded signals must exhibit sufficient duration and sustained
EEG
intensity. In standard scalp EEG recordings lasting 10–30 min and, epileptiform
discharges are detected in only 30–50% of patients (Pandian et al.,
ce of epileptiform EEG patterns is one of the primary indicators of epilepsy.
presen
2004). The
However, a recent systematic review with meta-analysis concluded that approximately 1.7% of individuals with epileptiform abnormalities do not experience
seizures (Aschner et al.,
2024).
In clinical practice, long-term video telemetry EEG (VT-EEG) is an important
presur
gical evaluation in patients with drug-resistant focal epilepsy, typically
conducted over several hours or days. By integrating simultaneous video recordings
of clinical manifestations, VT-EEG enables clinicians to classify and localize seizure
types in people with epilepsy and differentiate between epileptic and non-epileptic/
functional (also called ‘dissociative’
1
) seizures (Van Patten et al., 2025).
1
Defined as paroxysmal episodes of behavioural, sensory or motor changes.
354 B. Song
et al.
26.2 EEG Manifestations of Focal Epilepsy
Epileptiform EEG activity is typically categorized into three states: ictal, postictal,
and interictal, corresponding respectively to neural activity occurring during a
seizure, after a seizure, and between seizures (Fisher & Engel, 2010). The postictal
is conceptually defined as an abnormal period beginning at the termination of
state
an epileptic seizure and lasting until the brain returns to its baseline functional state.
When seizures recur, the interval between them is referred to as the interictal state.
26.2.1 Ictal EEG Patterns
In focal epilepsy, ictal discharges demonstrate characteristic changes in frequency,
amplitude, and morphology. The ictal EEG patterns are characterized by spatiotemporal evolution of focal rhythmic activity, which typically demonstrates a progressive increase in amplitude accompanied by a gradual slowing of frequency. This
activity may subsequently spread to neighbouring electrodes (Britton et al., 2016).
specific EEG patterns vary depending on the seizure focus. In the absence of any
The
subjective or objective clinical symptoms, such events are classified as subclinic al/
electrographic seizures.
Six major ictal patterns on scalp EEG (Foldvary et al., 2001), are summarized in
Table 26.1. Among the 566 seizures recorded from their cohort of 72 patients with
epilep
sy, rhythmic delta activity occurred more frequently in seizures arising from
the temporal lobe than from extratemporal regions. Theta activity was more commonly associated with mesial and neocortical temporal lobe epilepsy (TLE). There is
a higher likelihood of repetitive epileptiform activity in seizures originating from the
extratemporal lobe epilepsy. Moreover, generalized ictal onset patterns can be seen
in seizures emerging from the mesial frontal and occipital lobes.
Although researchers generally agree that rhythmic theta/delta activity is com-
present in mesial temporal lobe epilepsy, this pattern is not exclusive to
monly
temporal lobe pathology. For example, Baumgartner et al. reported that temporal
Table 26.1 Ictal onset patterns on scalp EEG
EEG pattern Definition
Rhythmic activity Alpha, theta, or delta frequencies
Paroxysmal fast Rhythmic activity> 13 Hz
Suppression Activity < 10 μv in amplitude
Repetitive epilepti-
form
activity
Arrhythmic activity Irregular, mixed-frequency waveforms
Obscured Pattern evolving from a
Text from Foldvary et al. (2001)
3 or more discharges in sequence
time, pattern, and distribution of onset were indiscernible
¼
¼
period obscured by artefact such that precise
26 EEG in Focal Epilepsy and Its Role in the Management of Adult Patients... 355
Fig. 26.2 Patterns of
seizure onset frequently
observed on scalp EEG. (a)
Rhythmical activity
evolving theta, delta, alpha
frequencies; (b) Rhythmical
spiking; (c) Spike waves; (d)
Electro-decremental onset
characterized by
low-voltage fast activity: (e)
Clinical seizure without a
clear EEG correlate.
(Adapted with permission
from Fisher et al.,
2014a, b)
intermittent rhythmic delta activity (TIRDA) may also be detected in lateral temporal
and orbitofrontal irritative zones (Baumgartner et al., 2025).
Examples of seizure onset patterns commonly observed on scalp EEG are
illustrated in Fig. 26.2. Importantly, the final conclusion about seizure/epilepsy
diagnosis should always be made based on clinical history and seizure semiology
and EEG provides additional supportive information (Fisher et al., 2014b).
Temporal lobe seizure onset often presents with rhythmic theta/delta activity
localized to the temporal region. Mesial temporal lobe epilepsy (MTLE) is one of
the most common forms of localization-related epilepsy, characterized by seizure
activity originating from the medial structures of the temporal lobe, including the
hippocampus, parahippocampal gyrus, and amygdala (Tatum, 2012 ). The electrophysiological hallmark of MTLE is the presence of rhythmic temporal theta or alpha
activity, typically in the 5–9 Hz range (Pataraia et al., 1998; Tatum, 2012 ).
Extratemporal seizures may exhibit features similar to those arising from the temporal lobe, and characterized by subtle rhythmic changes obscured by muscle
artefacts (Britton et al.,
2016).
Intracranial EEG (iEEG or icEEG) provides enhanced sensitivity compared with
scalp EEG and therefore reveals a wider range of ictal onset patt erns. The typical
characteristics of iEEG seizure-onset patterns are summarized in Fig.
26.3, with a
much greater signal-to-noise ratio and a wider frequency range compared to scalp
EEG (Alkawadri et al.,
2024; Lagarde et al., 2019; Perucca et al., 2014). In the study
by Lagarde et al., (Lagarde et al., 2019) low-voltage fast activity (LVFA) emerged
as one of the most common SEEG-defined seizure onset pattern in patients with drug
resistant focal epilepsy. LVFA is defined as rhythmic fast oscillations greater than
14 Hz with amplitudes below 30 μV, occurring without an initial overt change in
background activity. Although LVFA was observed across all etiological categories,
it was overrepresented in malformations of cortical development (MCD) and postvascular epilepsy. Notably, a burst of polyspikes preceding LVFA was identified
exclusively in patients with focal cortical dysplasia (FCD). In addition, LVFA was
356 B. Song et al.
Fig. 26.3 The eight seizure onset patterns from SEEG, with red asterisks marking the seizure onset
point. (a) Low-voltage fast activity (LVFA). (b) Preictal spiking that transitions into LVFA. (c) A
brief burst of polyspikes—high-frequency (>12 Hz), high-amplitude, short-duration activity lasting
less than 5 seconds—followed by LVFA. (d) A slow wave or baseline shift (comparable to a DC
shift), which precedes LVFA. (e) Rhythmic spikes and spike-wave discharges at low frequencies
above 6 Hz and consistently below 14 Hz. (f) Sharp theta or alpha activity, represented by
low-frequency sinusoidal waveforms. (g) Sharp beta-frequency activity with beta-band sinusoidal
oscillations. (h) A delta-brush pattern characterized by bursts of low-amplitude, rapid gammafrequency activity superimposed on low-frequency delta sinusoidal waves. (Adapted with permission from Lagarde et al.,
2019)
often followed by a DC shift or slow wave, particularly in cases involving extensive
networks of epileptogenic zones (Lagarde et al., 2019).
26.2.2 Interictal EEG Patterns
The interictal EEG plays a crucial role in the diagnosis of epilepsy specifically
assisting in establishing the presence of epilepsy, differentiating between focal and
generalized seizure disorders, and defining specific epilepsy syndromes (Pillai &
Sperling,
abnorm
ing both therapeutic strategies and prognostic evaluation.
interictal epileptiform discharges (IEDs) as isolated waveforms characterized by a
di- or tri-phasic shape with a sharply pointed peak, a duration that differs from the
surrounding background activity, and a distinctly asymmetric morphology (Kural
et al.,
ongoin
topography demonstrates a coherent distrib ution of negative and positive potentials
consistent with a cortical generator oriented radially, obliquely, or tangentially. A
2006). Precise characterization and localization of interictal epileptiform
alitiesis essential for identifying specific epileptic syndromes, thereby guid-
The Inter
national Federation of Clinical Neurophys iology (IFCN) defines
2020). They are typically followed by a slow after-wave and disrupt the
g background rhythm at the time of their occurrence. In addition, their voltage
26 EEG in Focal Epilepsy and Its Role in the Management of Adult Patients... 357
spike is defined as a transient with a duration of 20–70 ms, whereas a sharp wave has
a duration of 70–200 ms (Kane et al.,
2017).
The accurate interpretation of interictal activity requires caution, as certain benign
EEG variants may mimic epileptiform discharges and lead to potential
misclassification. Notably, some benign EEG variants, normally with unknown
aetiology, can appear as patterns resembling epileptiform EEG activity, for example:
Small sharp spikes of sleep (SSS), wicket spikes, subclinical rhythmic electrographic
discharges in adults (SREDA), rhythmic mid-temporal discharges (RMTD), and Mu
rhythm (White et al.,
resemb
le subclinical seizures (Westmoreland & Klass, 1997).
1977). Sometimes a burst of repeating SREDA can evolve to
In focal epilepsies, interictal spike s, when interpreted alongside other neuroimaging
findings, can provide valuable guidance for localizing the seizure onset zone.
The sensitivity of detecting IEDs using scalp EEG must be carefully considered.
Goodin et al. reported that 29–55% of patients exhibited positive EEG findings for
IEDs (Goodin et al., 1990). When the duration of scalp EEG monitoring is extended
recordings are repeated, sensitivity can increase substantially, reaching 80–90%
and
(Salinsky et al., 1987 ). Tao et al. demonstrated that at least 10 cm
2
of temporally
overlapping cortical activity is required to generate a scalp-recordable EEG spike in
humans (Tao et al., 2011).
By contrast, IEDs are highly prevalent in iEEG, with reported sensitivities of
85
–95% (Lee et al., 2023; Nayak et al., 2004), compared with the considerably lower
and more variable rates observed with scalp EEG (approximately 24–55% across
studies) in patients with drug-resistant focal epilepsy (Goodin et al., 1990; Perucca
al., 2014). Because the duration of an iEEG investigation is primarily driven by
et
the
need to record a sufficient number of seizures to reliably delineate the seizureonset zone, the sensitivity for detecting ictal electrophysiological activity is correspondingly high, though still influenced by peri-surgical factors (Bottan et al., 2023;
iah et al., 2025).
Muth
26.3 Localization of Ictal and Interictal EEG Events
The localization of the epileptogenic zone is a principal objective of EEG recording.
The cortical region generating IEDs and the distribution of surface ictal changes are
generally broader than the actual cortical area where clinical seizures originate
(So et al.,
tance
are frequently recorded in patients with unilateral TLE (So et al.,
Pacia reported that nearly all of their cases exhibited a regular 5–9 Hz EEG rhythm,
typically lasting for more than five seconds and primarily localized to the
subtemporal and temporal electrodes (Ebersole & Pacia,
that
within the temporal neocortex.
1989). Since the last century, neurologists have emphasized the impor-
of identifying ictal EEG patterns for accurate localization, as bilateral spikes
Ebersole and
1989).
1996). They also noted
lateralized polymorphic activity in the 2–5 Hz range may indicate seizure onset