Sleep Recording is important for clinical diagnosis and treatment of sleep disorders. Sleep staging is one of the most important steps in sleep analysis and is typically performed based on the characteristics of electrophysiological signals including EEG, EOG, and EMG. Normal healthy sleep consists of sequences of stages. According to the traditional Rechtschaffen & Kales (R&K) rules, these stages include: Awake, Light Sleep, Deep Sleep, and Rapid-Eye Movement (REM) Sleep. Our study develops a simple four-stage process to classify sleep into wakefulness, stage 1, stage 2, slow wave sleep (SWS) and rapid eye movement (REM) sleep based on single LEOG channel. To achieve this goal, this study first generates feature variables from LEOG signal. The proposed feature selection method is applied to select a subset of features to improve the accuracy of the classifier. By applying the proposed approach to 48727 distinct LEOG epochs that are obtained from 62 subjects, the accuracy rate is about 72.6%. The largest amount of errors occurs in the identification of Stage 1, 56.3% of which was misclassified into stages 2 or wake. The second largest error is associated with REM sleep, 23.7% of which was misclassified into stages 2.

This study aims at sleep stage classification problem via EOG signals. The classification problem consists of four steps. The first step is to distinguish slow wave sleep from the rest of the sleep periods. Wake periods are identified in the second step. The third step finds REM sleep and the last step classifies stage 2 and stage1 sleep. By using different EOG signal features in different steps of the classification process, this work uses back-propagation trained neural networks to perform classification. With the exception of stage 1 sleep, the sensitivity and positive predictive value ranges from 70% to 80%. The overall classification accuracy is 74.80%.

Blink waveform in electrooculogram (EOG) data was used to develop and adjust the method of drowsiness detection in drivers. The origins of some other waveforms in EOG signal were not very clearly understood. The purpose of this thesis work is to study the EOG signal and give explanation of different kind of waveforms in EOG signal, and give suggestions to improve the blink detection algorithm. The road driving test video records and synchronized EOG signal were used to build an EOG library. By comparing the video record of the driver’s face and the EOG data, the origin of the unknown waveforms were discovered and related with the driver’s behavior. Literature descriptions were given to explain the EOG signal. The EOG library is the main result of this project. It organized by different types of EOG signal. Description and explanation were given for each type of waveform, as well as some examples. The knowledge gained from the previous research review and the EOG library gives some improvement suggestions for the blink detection algorithm. These suggestions still need to be verified in practical way.

Made available in DSpace on 2018-08-02T00:00:34Z (GMT). No. of bitstreams: 1 tese_10127_Dissertação-de-Mestrado-Alexandre-Bissoli-Versão-Final.pdf: 5108283 bytes, checksum: a4a9dde79a52505e6a3c7b70f49dbb0c (MD5) Previous issue date: 2016-07-29 / Pessoas com deficiência têm dificuldade de interagir com o ambiente onde vivem, devido às próprias limitações inerentes à sua deficiência. Atividades simples como ligar lâmpada, ventilador, televisão ou qualquer outro equipamento, de forma independente, pode ser impossível para esse grupo de pessoas. Este trabalho apresenta um sistema assistivo multimodal para controlar um ambiente inteligente por meio de sinais biológicos. Os usuários em potencial deste sistema são indivíduos com deficiências motoras graves, que desejam adquirir mais autonomia dentro do ambiente doméstico. Os sinais biológicos utilizados são sEMG, EOG e VOG. Isso possibilitou dois tipos de interação: uma empregando gestos faciais e movimento dos olhos, e a outra utilizando o rastreamento das fixações do olhar (eye/gaze tracking). Um diferencial importante deste trabalho é a utilização de dispositivos convencionais de baixo custo, fácil manuseio e de rápida configuração. No primeiro bloco de testes, o objetivo era avaliar o desempenho do sistema utilizando o Emotiv EPOC e o Eye Tracker, comparando a Taxa de Transferência de Informação (ITR) e a Utilidade (U) de ambas as Interfaces Humano-Máquina (IHM) desenvolvidas para controlar o Ambiente Inteligente. Para esses testes foram pré-estabelecidas cinco tarefas, as quais foram realizadas por dez voluntários. No segundo bloco de testes, o objetivo era avaliar a usabilidade (SUS) e o desempenho (GAS) do sistema do ponto de vista do usuário, utilizando o Eye Tracker em três aplicações diferentes: Controle do Ambiente Inteligente (AI), Comunicação Aumentativa e Alternativa (CAA) e Ambiente Virtual (AV). Os testes foram realizados por 17 voluntários (sendo dois com deficiência em todas as aplicações) e foram pré-estabelecidas 5, 5 e 18 tarefas para as três aplicações (AI, CAA e AV, respectivamente). Com relação aos resultados da avaliação de desempenho, observou-se que 15 dos 17 participantes obtiveram resultado esperado ou superior ao esperado logo na primeira utilização. Este resultado ainda pode ser melhorado, à medida que o participante obtiver maior familiaridade com o sistema.

One primary topic of sleep studies is the depth of sleep. According to definitions of R&K rules, human sleep can be roughly divided into three different stages: Awake, Non-rapid-eye-movement (NREM) Sleep, and Rapid-eye-movement (REM) Sleep. Moreover, sleep stages are scored mainly by EEG signals and complementally by EOG and EMG signals. Many researchers have indicated that diseases or disorders occur during sleep will affect life quality of patients. For example, REM sleep-related dyssomnia is highly correlated with neurodegenerative or mental disorders such as major depression. Furthermore, sleep apnea is one of the most common sleep disorders at present. Untreated sleep apnea can increase the risk of mental and cardiovascular diseases. This research proposes a detection method of REM sleep. Take into account the environment of homecare, we just extract and analyze EOG signals for the sake of convenience in comparison with EEG channels. By analyzing elementary waveforms of EOG signals based on VQ method, the proposed method performs a classification accuracy of 67.71% in a group application. The corresponding sensitivity and specificity are 73.38% and 68.95% respectively. In contrast, the average classification accuracy is 82.02% in personalized applications. And the corresponding average sensitivity and specificity are 83.05% and 81.62% respectively. Experimental results demonstrate the feasibility of detecting REM sleep via the proposed method, especially in personalized applications. This will be propitious to a long term tracing and research of personal sleep status.

The rapid-eye-movement (REM) sleep is one of the most important parts in overnight sleep. In this study, an automatic REM sleep staging rule is introduced. Compared with the traditional REM detection method, a distinct feature of this method is that it only requires two EOG signals and thus reduces the number of input signal channels significantly. We calculate the correlation coefficient series between two EOG signals. By representing such a series with a VQ coding method, several techniques are proposed to improve the classification rate. Experimental results are given to demonstrate the effectiveness of the proposed approach.

Performance lapses in occupations where public safety is paramount can have disastrous consequences, resulting in accidents with multiple fatalities. Drowsy individuals performing an active task, like driving, often cycle rapidly between periods of wake and sleep, as exhibited by cyclical variation in both EEG power spectra and task performance measures. The aim of this project was to identify reliable physiological cues indicative of lapses, related to behavioural microsleep episodes, from the EEG, which could in turn be used to develop a real-time lapse detection (or better still, prediction) system. Additionally, the project also sought to achieve an increased understanding of the characteristics of lapses in responsiveness in normal subjects. A study was conducted to determine EEG and/or EOG cues (if any) that expert raters use to detect lapses that occur during a psychomotor vigilance task (PVT), with the subsequent goal of using these cues to design an automated system. A previously-collected dataset comprising physiological and performance data of 10 air traffic controllers (ATCs) was used. Analysis showed that the experts were unable to detect the vast majority of lapses based on EEG and EOG cues. This suggested that, unlike automated sleep staging, an automated lapse detection system needed to identify features not generally visible in the EEG. Limitations in the ATC dataset led to a study where more comprehensive physiological and performance data were collected from normal subjects. Fifteen non-sleep-deprived male volunteers aged 18-36 years were recruited. All performed a 1-D continuous pursuit visuomotor tracking task for 1 hour during each of two sessions that occurred between 1 and 7 weeks apart. A video camera was used to record head and facial expressions of the subject. EEG was recorded from electrodes at 16 scalp locations according to the 10-20 system at 256 Hz. Vertical and horizontal EOG was also recorded. All experimental sessions were held between 12:30 and 17:00 hours. Subjects were asked to refrain from consuming stimulants or depressants, for 4 h prior to each session. Rate and duration were estimated for lapses identified by a tracking flat spot and/or video sleep. Fourteen of the 15 subjects had one or more lapses, with an overall rate of 39.3 ± 12.9 lapses per hour (mean ± SE) and a lapse duration of 3.4 ± 0.5 s. The study also showed that lapsing and tracking error increased during the first 30 or so min of a 1-h session, then decreased during the remaining time, despite the absence of external temporal cues. EEG spectral power was found to be higher during lapses in the delta, theta, and alpha bands, and lower in the beta, gamma, and higher bands, but correlations between changes in EEG power and lapses were low. Thus, complete lapses in responsiveness are a frequent phenomenon in normal subjects - even when not sleep-deprived - undertaking an extended, monotonous, continuous visuomotor task. This is the first study to investigate and report on the characteristics of complete lapses of responsiveness during a continuous tracking task in non-sleep-deprived subjects. The extent to which non-sleep-deprived subjects experience complete lapses in responsiveness during normal working hours was unexpected. Such findings will be of major concern to individuals and companies in various transport sectors. Models based on EEG power spectral features, such as power in the traditional bands and ratios between bands, were developed to detect the change of brain state during behavioural microsleeps. Several other techniques including spectral coherence and asymmetry, fractal dimension, approximate entropy, and Lempel-Ziv (LZ) complexity were also used to form detection models. Following the removal of eye blink artifacts from the EEG, the signal was transformed into z-scores relative to the baseline of the signal. An epoch length of 2 s and an overlap of 1 s (50%) between successive epochs were used for all signal processing algorithms. Principal component analysis was used to reduce redundancy in the features extracted from the 16 EEG derivations. Linear discriminant analysis was used to form individual classification models capable of detecting lapses using data from each subject. The overall detection model was formed by combining the outputs of the individual models using stacked generalization with constrained least-squares fitting used to determine the optimal meta-learner weights of the stacked system. The performance of the lapse detector was measured both in terms of its ability to detect lapse state (in 1-s epochs) and lapse events. Best performance in lapse state detection was achieved using the detector based on spectral power (SP) features (mean correlation of φ = 0.39 ± 0.06). Lapse event detection performance using SP features was moderate at best (sensitivity = 73.5%, selectivity = 25.5%). LZ complexity feature-based detector showed the highest performance (φ = 0.28 ± 0.06) out of the 3 non-linear feature-based detectors. The SP+LZ feature-based model had no improvement in performance over the detector based on SP alone, suggesting that LZ features contributed no additional information. Alpha power contributed the most to the overall SP-based detection model. Analysis showed that the lapse detection model was detecting phasic, rather than tonic, changes in the level of drowsiness. The performance of these EEG-based lapse detection systems is modest. Further research is needed to develop more sensitive methods to extract cues from the EEG leading to devices capable of detecting and/or predicting lapses.

We hypothesized that temporal features of EEG are altered in sleep apnea subjects comparedto normal subjects. The initial aim was to develop a measure to discriminate sleep stages innormals. The longer-term goal was to apply these methods to identify differences in EEGactivity in sleep apnea subjects from normals. We analyzed the C3A2 EEG and anelectrooculogram (EOG) recorded from 9 normal adults awake and in rapid eye movement(REM) and non-REM sleep. The EEG signals were filtered to remove EOG contamination. Twomeasures of the irregularity of EEG signals, Sample Entropy (SpEn) and Tsallis Entropy, wereevaluated for their ability to discriminate sleep stages. SpEn changes with sleep state, beinglargest in Wake. Stage 3/4 had the smallest SpEn (0.57??0.11) normalized to Wake values,followed by Stage 2 (0.72??0.09), REM (0.75??0.1) and Stage 1 (0.89??0.05). This pattern wasconsistent in all the polysomnogram records analyzed. Similar pattern was observed in leadO1A2 as well. We conclude that SpEn may be useful as part of a montage for assessing sleepstate. We analyzed data from sleep apnea subjects having obstructive and central apnea eventsand have made some preliminary observations; the SpEn values were more similar across sleepstages and also high correlation with oxygen saturation was observed.

Über 50 % der 65- bis 80-Jährigen leiden unter einer verminderten Riechfähigkeit. Strukturelle Veränderungen in der Riechbahn sind wahrscheinliche Gründe. Der altersbedingte histologische Rückgang des olfaktorischen Epithels wird seit langem erforscht. Die Darstellung der altersbedingten peripheren und zentralen Veränderungen auf elektrophysiologischer Ebene ist Thema der vorliegenden Arbeit. Insgesamt wurden 73 TeilnehmerInnen untersucht, darunter 40 jüngere (davon 25 Frauen, Altersspanne 18-27 Jahre) und 33 ältere (davon 22 Frauen, Altersspanne 50-78 Jahre). Vor der Teilnahme an der elektrophysiologischen Untersuchung erhielten alle ProbandInnen eine nasale Endoskopie, eine standardisierte Anamnese sowie detaillierte Geruchstests mittels Sniffin‘ Sticks. ProbandInnen mit chronischen Erkrankungen des olfaktorischen Systems oder anderen Bereichen des zentralen Nervensystems wurden nicht in die Studie eingeschlossen. Zur intranasalen Stimulation wurden olfaktorische und trigeminale Reize verwendet. Als olfaktorische Stimulantien dienten Schwefelwasserstoff als eher unangenehmer und 2-Phenylethylalkohol als eher angenehmer Duft. Zur trigeminalen Stimulation diente Kohlendioxid. Mittels Luftverdünnungsolfaktometrie (Olfaktometer OM6b; Burghart, Deutschland) wurden Stimuli von 500 ms Dauer in einen konstanten Luftstrom von etwa 8 l/min eingebettet. Elektroolfaktogramme wurden als elektrophysiologisches Korrelat der olfaktorischen Rezeptorpotenziale direkt von der Riechschleimhaut abgeleitet. Gleichzeitig erfolgte die EEG-basierte Registrierung chemosensorisch-ereigniskorrelierter Potentiale (CSEP) bzw. bei Reizung mit Duftstoffen olfaktorisch- ereigniskorrelierter Potenziale (OERP). Die Ergebnisse der psychophysischen Riechtests ergaben eine deutlich negative Korrelation mit dem Alter der Teilnehmenden (r=-0,42, p< 0,001). Obwohl die EOG-Amplituden und Latenzen keinen signifikanten Unterschied zwischen den Altersgruppen zeigten, wurde ein deutlicher altersabhängiger Rückgang in der Anzahl der registrierten Potenziale festgestellt. Für die zentralnervöse CSEP-Reaktion zeigte sich eine altersabhängige Verkürzung der relevanten Latenzen, so z.B. die P1-Latenz an Elektrodenposition Cz nach Stimulation mit PEA (t = 3,4, p = 0,003). Eine Korrelation von P2-Ampltiuden (Cz) und Alter bei Stimulation mit PEA zeigte darüber hinaus einen signifikant negativen Zusammenhang (p=0,005, r=-0,44). Die Veränderungen der EOG und CSEP entsprachen in ihrer Ausprägung nicht den altersabhängigen Veränderungen in den psychophysischen Testergebnissen. Jedoch zeigen die vorliegenden Ergebnisse einen eher zentral-betonten, aber auch peripher nachweisbaren Alterungsprozess der elektrophysiologischen Geruchsverarbeitung. Die geringe Ausprägung dieser Veränderungen ist möglicherweise Zeichen der Effektivität von Kompensationsmechanismen und systemimmanenten Redundanzen, die es weiter zu erforschen gilt.

A low-cost circuit board design is presented, which in one embodiment is smaller than a credit card, for biopotential (EMG, ECG, or EEG) data acquisition, with a focus on EEG for brain-computer interface applications. The device combines signal conditioning, low-noise and high-resolution analog-to-digital conversion of biopotentials, user motion detection via accelerometer and gyroscope, user-programmable digital pre-processing, and data transmission via Bluetooth communications. The full development of the device to date is presented, spanning three embodiments. The device is presented both as a functional data acquisition system and as a template for further development based on its publicly-available schematics and computer-aided design (CAD) files. The design will be made available at the GitHub repository https://github.com/kellygs/eeg.