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    • At a later point of time when the

    2018-11-01

    At a later point of time, when the fastest arriving stimulus used in a previous associative learning event reaches the nervous system, it reactivates the IPL and activates the inter-LINKed postsynaptic terminal from a lateral direction (Fig. 1D). When the postsynaptic terminal is in a background state of continuous depolarization induced by the neurotransmitter molecules released from its presynaptic terminal (Fig. 1A), any “incidental” lateral activation of the postsynaptic terminal through the IPL is expected to induce a semblance of the arrival of activity from its presynaptic terminal as a systems property (see Fig. 1 for further description). Semblance is an expected event of cellular hallucination induced at the inter-LINKed postsynaptic terminal [19,20]. Such an operation is essential for inducing the function of internal sensations; for example, memory [22]. Another integral part of the system property is that the lateral entry of activity through the IPL can contribute to the horizontal component of the oscillating potentials recorded extracellularly [23]. During the wake state, maintaining the extracellularly-recorded oscillating potentials at a certain frequency range is essential for inducing the internal sensations of various higher pituitary adenylate cyclase-activating peptide functions. For example, as the background frequency recorded by the electro-encephalogram (EEG) waves is reduced, there is a gradual reduction in the level of awareness that eventually leads to a state of unconsciousness [24].
    Theoretical findings
    Conclusion
    Competing interests
    Acknowledgements Author acknowledges the support from Neurosearch Center (Grant number: 3:24/2014).
    Introduction We present a case trial of sleep stage detection using a novel electroencephalographic (EEG) recording platform placed in the ear (ear-EEG). In current practice, polysomnography (PSG) during hospitalization is the gold standard for diagnosing sleep disorders. However, PSG is also a relatively resource intensive investigation, which limits its use. Home PSG as a less expensive alternative has been investigated finding unsatisfactory recordings in 13 of 48 patients, the main cause of unsatisfactory recordings being loss of EEG data [1]. We envision that ear-EEG fills this gap. The ear-EEG devices are customized to each user similar to hearing-aid earplugs. This secures tight contact between the recording electrodes and the skin. Ear-EEG as a method has been tested in awake, healthy subjects using a variety of known EEG paradigms [2]. Compared to scalp EEG it maintains a similar signal-to-noise ratio, being less contaminated by electro-muscular artifacts albeit at a cost to signal amplitudes [3]. Correlation analysis between ear-EEG and scalp-EEG shows the highest degree of similarity for scalp electrodes near the temporal region decreasing towards the midline [4]. Thus one can make reasonable assumptions that sleep related phenomena predominantly found near the midline, such as vertex sharp waves, would be less likely to be resolved with ear-EEG.
    Methods The ear-EEG device consists of four recording electrodes embedded in a solid cast made from individually fitted ear impression moulds. We used two ear-EEG devices simultaneously. The recording electrodes are labelled E, I, B and A (Fig. 1). We recorded simultaneous scalp EEG for comparison. Scalp electrode configuration followed the international 10–20 system with 25 electrodes including inferior temporal chain and EOG. For sleep staging, we used electrode positions F3, C3, O1, F4, C4, O2 contralaterally referenced to M1 and M2 on the mastoid processes as recommended by The American Academy of Sleep Medicine [5]. The study has been approved by the National Board of Health and the Local Ethics Committee. The validation setup is 3 fold: The subject was a 30 y.o. male with no known medical problems who participated after giving informed consent. Ear-EEG and simultaneous scalp EEG were recorded for 21h including one night’s sleep. The amplifier used was a Nicolet wireless 64-channel system sampling at 256Hz. Both scalp and ear-EEG electrodes was connected to the same amplifier. For visual inspection of raw data we used proprietary software (Nervus reader v.5.93.424, from Cephalon, Denmark). For frequency analysis of the EEG we calculated power spectrograms in Matlab using the pwelch function. Ear-EEG signals have smaller amplitudes compared to standard EEG. To compensate we displayed scalp signals at 100 µV/cm and ear-EEG signals at 30µV/cm. This preferentially amplified low frequency noise in ear-EEG channels making parts of the EEG difficult to read due to high-amplitude waveforms. To offset this effect we displayed duplicates of ear-EEG channels with different low cut filters, one at 2.0Hz and the other at 0.5Hz. Using the latter is necessary for identification of the slow waves of N3 sleep, but using a low cut at 2.0Hz improved the readability of parts of the EEG where faster frequencies predominated. We found this approach to be more efficient than dynamically altering the sensitivity throughout the sleep staging process.