“…TMI generally generates beta ERS above the motor cortex; however, beta ERD above the left and right central areas is also often observed in prior studies [41, 45, 46]. Finally, several previous studies have reported that the mental arithmetic tasks enhance the frontal midline theta and weaken the frontal alpha [46–49], which is consistent with our findings.Fig. 4Time–frequency maps and topographical distribution for each mental task.…”
BackgroundBrain–computer interfaces (BCIs) have demonstrated the potential to provide paralyzed individuals with new means of communication, but an electroencephalography (EEG)-based endogenous BCI has never been successfully used for communication with a patient in a completely locked-in state (CLIS).MethodsIn this study, we investigated the possibility of using an EEG-based endogenous BCI paradigm for online binary communication by a patient in CLIS. A female patient in CLIS participated in this study. She had not communicated even with her family for more than one year with complete loss of motor function. Offline and online experiments were conducted to validate the feasibility of the proposed BCI system. In the offline experiment, we determined the best combination of mental tasks and the optimal classification strategy leading to the best performance. In the online experiment, we investigated whether our BCI system could be potentially used for real-time communication with the patient.ResultsAn online classification accuracy of 87.5% was achieved when Riemannian geometry-based classification was applied to real-time EEG data recorded while the patient was performing one of two mental-imagery tasks for 5 s.ConclusionsOur results suggest that an EEG-based endogenous BCI has the potential to be used for online communication with a patient in CLIS.
“…TMI generally generates beta ERS above the motor cortex; however, beta ERD above the left and right central areas is also often observed in prior studies [41, 45, 46]. Finally, several previous studies have reported that the mental arithmetic tasks enhance the frontal midline theta and weaken the frontal alpha [46–49], which is consistent with our findings.Fig. 4Time–frequency maps and topographical distribution for each mental task.…”
BackgroundBrain–computer interfaces (BCIs) have demonstrated the potential to provide paralyzed individuals with new means of communication, but an electroencephalography (EEG)-based endogenous BCI has never been successfully used for communication with a patient in a completely locked-in state (CLIS).MethodsIn this study, we investigated the possibility of using an EEG-based endogenous BCI paradigm for online binary communication by a patient in CLIS. A female patient in CLIS participated in this study. She had not communicated even with her family for more than one year with complete loss of motor function. Offline and online experiments were conducted to validate the feasibility of the proposed BCI system. In the offline experiment, we determined the best combination of mental tasks and the optimal classification strategy leading to the best performance. In the online experiment, we investigated whether our BCI system could be potentially used for real-time communication with the patient.ResultsAn online classification accuracy of 87.5% was achieved when Riemannian geometry-based classification was applied to real-time EEG data recorded while the patient was performing one of two mental-imagery tasks for 5 s.ConclusionsOur results suggest that an EEG-based endogenous BCI has the potential to be used for online communication with a patient in CLIS.
“…In particular, some studies reported no significant difference in the theta band activity between HCs and FEP ( 87 ), FES ( 73 , 76 , 77 ), or HR ( 87 ) subjects. Other studies reported an increase in theta band activity in FES subjects compared to HCs ( 75 , 88 ) during resting conditions. The increase in theta activity in FES was associated with the severity of negative symptoms ( 88 ) and this alteration was observed also in HR-T if compared to HR-NT and HCs ( 79 ).…”
Section: Resultsmentioning
confidence: 86%
“…Other studies reported an increase in theta band activity in FES subjects compared to HCs ( 75 , 88 ) during resting conditions. The increase in theta activity in FES was associated with the severity of negative symptoms ( 88 ) and this alteration was observed also in HR-T if compared to HR-NT and HCs ( 79 ). Abnormalities in theta oscillations were also found in studies that performed a source localization analysis through LORETA ( 78 , 89 ).…”
Introduction: Electrophysiological (EEG) abnormalities in subjects with schizophrenia have been largely reported. In the last decades, research has shifted to the identification of electrophysiological alterations in the prodromal and early phases of the disorder, focusing on the prediction of clinical and functional outcome. The identification of neuronal aberrations in subjects with a first episode of psychosis (FEP) and in those at ultra high-risk (UHR) or clinical high-risk (CHR) to develop a psychosis is crucial to implement adequate interventions, reduce the rate of transition to psychosis, as well as the risk of irreversible functioning impairment. The aim of the review is to provide an up-to-date synthesis of the electrophysiological findings in the at-risk mental state and early stages of schizophrenia.Methods: A systematic review of English articles using Pubmed, Scopus, and PsychINFO was undertaken in July 2020. Additional studies were identified by hand-search. Electrophysiological studies that included at least one group of FEP or subjects at risk to develop psychosis, compared to healthy controls (HCs), were considered. The heterogeneity of the studies prevented a quantitative synthesis.Results: Out of 319 records screened, 133 studies were included in a final qualitative synthesis. Included studies were mainly carried out using frequency analysis, microstates and event-related potentials. The most common findings included an increase in delta and gamma power, an impairment in sensory gating assessed through P50 and N100 and a reduction of Mismatch Negativity and P300 amplitude in at-risk mental state and early stages of schizophrenia. Progressive changes in some of these electrophysiological measures were associated with transition to psychosis and disease course. Heterogeneous data have been reported for indices evaluating synchrony, connectivity, and evoked-responses in different frequency bands.Conclusions: Multiple EEG-indices were altered during at-risk mental state and early stages of schizophrenia, supporting the hypothesis that cerebral network dysfunctions appear already before the onset of the disorder. Some of these alterations demonstrated association with transition to psychosis or poor functional outcome. However, heterogeneity in subjects' inclusion criteria, clinical measures and electrophysiological methods prevents drawing solid conclusions. Large prospective studies are needed to consolidate findings concerning electrophysiological markers of clinical and functional outcome.
“…A number of resting-state EEG studies have been conducted on patients with schizophrenia (vs. healthy controls) most often with eyes closed (although see Venables et al, 2009; Hanslmayr et al, 2013; Narayanan et al, 2014 for three eyes open studies). In total, 37 schizophrenia studies were identified for this review (Clementz et al, 1994; Sponheim et al, 1994; Wada et al, 1994; Omori et al, 1995; Pascual-Marqui et al, 1999; Begić et al, 2000; Harris et al, 2001; Knott et al, 2001a; Wuebben and Winterer, 2001; Mientus et al, 2002; Veiga et al, 2003; Kirino, 2004; Harris et al, 2006; Kirino, 2007; Knyazeva et al, 2008; Tislerova et al, 2008; John et al, 2009; Venables et al, 2009; Bandyopadhyaya et al, 2011; Begić et al, 2011; Itoh et al, 2011; Schug et al, 2011; Hong et al, 2012; Hanslmayr et al, 2013; Kam et al, 2013; Narayanan et al, 2014; Ranlund et al, 2014; Tikka et al, 2014; Andreou et al, 2015; Garakh et al, 2015; Goldstein et al, 2015; Kim et al, 2015; Mitra et al, 2015; Shreekantiah Umesh et al, 2016; Mitra et al, 2017; Moeini et al, 2017; Baradits et al, 2018). The median sample size was 63 (range 26–425), with the average age of participants being 31 years old.…”
A significant proportion of the electroencephalography (EEG) literature focuses on differences in historically pre-defined frequency bands in the power spectrum that are typically referred to as alpha, beta, gamma, theta and delta waves. Here, we review 184 EEG studies that report differences in frequency bands in the resting state condition (eyes open and closed) across a spectrum of psychiatric disorders including depression, attention deficit-hyperactivity disorder (ADHD), autism, addiction, bipolar disorder, anxiety, panic disorder, post-traumatic stress disorder (PTSD), obsessive compulsive disorder (OCD) and schizophrenia to determine patterns across disorders. Aggregating across all reported results we demonstrate that characteristic patterns of power change within specific frequency bands are not necessarily unique to any one disorder but show substantial overlap across disorders as well as variability within disorders. In particular, we show that the most dominant pattern of change, across several disorder types including ADHD, schizophrenia and OCD, is power increases across lower frequencies (delta and theta) and decreases across higher frequencies (alpha, beta and gamma). However, a considerable number of disorders, such as PTSD, addiction and autism show no dominant trend for spectral change in any direction. We report consistency and validation scores across the disorders and conditions showing that the dominant result across all disorders is typically only 2.2 times as likely to occur in the literature as alternate results, and typically with less than 250 study participants when summed across all studies reporting this result. Furthermore, the magnitudes of the results were infrequently reported and were typically small at between 20% and 30% and correlated weakly with symptom severity scores. Finally, we discuss the many methodological challenges and limitations relating to such frequency band analysis across the literature. These results caution any interpretation of results from studies that consider only one disorder in isolation, and for the overall potential of this approach for delivering valuable insights in the field of mental health.
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