Neurometric Topographic Mapping of EEG and Evoked Potential Features: Application to Clinical Diagnosis and Cognitive Evaluation

Electrophysiology and intelligence: the electrophysiology of intellectual functions in intellectual disability

“…Petsche et al 1986). However, it must be mentioned that an abnormal increase of the highest frequencies has been observed in several other psychiatric pathologies, such as depression, alcoholism or schizophrenia, and has been interpreted as reflecting a cognitive deficit particularly related to attentional aspects if the distribution is mainly over posterior regions (Ray & Cole 1985; Gruzelier & Liddiard 1989; John et al 1989), which could be advanced to explain the present findings.…”

“…However, this finding is not restricted to subjects with ID since several other pathologies have also been seen to show this unusual pattern for the beta band (e.g. depression; see John et al 1989).…”

Correlation of qEEG with PET in Schizophrenia

“…The well known entity of pathological polymorphic delta has been correlated with anoxia and functional deafferentation of the cortex [23,24], and often leads to the assumption of a general association of delta activity with depressed cerebral metabolism. However, approximately 20-30% of the voltage of awake normal humans in anterior leads is in delta as revealed by multiple replicated studies [17][18][19][20][21][22]. Increased delta activity has been correlated with a number of presumably metabolically activating states in normal awake humans such as the performance of calculations [25], reaction time tests [26], abstract thought [27] or an omitted stimulus paradigm [28].…”

“…Such an approach increases the confidence limits of the normal data which mitigates against falsely positive values deriving from overly rigid or specific testing parameters. The multiple replications of the neurometric norms [17][18][19][20][21][22] provide substantiative empirical justification for this approach. In the present study, the inclusion of the incidental effects of differing laboratory environments in the confidence limits of the norms against which the qEEG data was referenced may have mitigated against spurious changes due to laboratory environment.…”

Classification of Quantitative EEG Data by an Artificial Neural Network: A Preliminary Study

“…The second, more general problem is how to manage the large number of variables that the computational anal ysis of biological signals provides, and to apply statistics to them in a way that permits using the method in research and clinical practice. Some of these problems are: (1) intrinsic variability and methodological artifacts that result in considerable intra-and intersubject variabil ity [1, 2]; (2) the existence of interdependence and coli nearity between channels [3]; (3) usually the variables obtained from healthy subjects do not follow the normal distribution [4,5] [5,7], but there is no general agreement about which is the most correct or the best [3. 6.…”