Shigeto Nishida

Signal separation of background EEG and spike by using morphological filter

A signal separation method for extracting background electroencephalogram (EEG) from EEG containing spikes was proposed. Morphological filters were designed for extracting spike waveforms, and then the background EEG was obtained by subtracting the detected spike waveforms from the EEG with spike. The proposed method was evaluated by using simulated EEG data, which consisted of a summation of EEG without spike and model waveform of typical spike. The background EEG separated by the method was processed by the automatic background EEG interpretation.

Method for single-trial recordings of somatosensory evoked potentials

Evoked potentials (EPs) in response to stimuli are recorded from a human scalp contaminated with noise. To improve the signal-to-noise ratio, averaging methods have been widely used for the recorded data. However, when the waveforms of EP for each stimulus are not identical, the average waveform of the EP deteriorates. Variation of the EP waveform to each stimulus itself is important information for the EP. In this paper, a recording method for single somatosensory evoked potential (SEP) waveform is proposed, in which three kinds of band-pass filters were selectively used during three specific time sectors for each interstimulus interval. For the late section of the interval, an EEG waveform prediction method was applied to eliminate contaminated alpha rhythm components. By using the proposed method, we were successful in detecting the single SEP waveform.

Wave form decomposition of giant SEP and its computer model for scalp topography

By using the decomposition technique developed by ourselves to investigate the scalp topography of evoked potentials, a computer model for the scalp topography of giant SEPs was computed from 5 patients with progressive myoclonic epilepsy and was compared with those obtained from 6 normal subjects. Components of giant SEPs were similar to those of normal SEPs with respect to various parameters, although the former were much larger than the latter. An experimental enlargement of some of the early cortical components of the normal SEP model gave rise to a wave form closely resembling that of the giant SEP. These findings support our previous conclusion, derived from study of the scalp topography of the original SEP wave form, that the giant SEP results from a pathological enhancement of certain early cortical components of the normal SEP. The underlying neuronal hyperexcitability seems to involve more than one subunit of the sensorimotor cortex.

Estimate of physiological variability of peak latency in single sweep P300.

Among single sweep records of event-related potentials (ERPs), the peak latency of P300, which is one of the most prominent positive peaks in the ERP obtained in the oddball paradigm, may vary depending on the conditions of the subject. In the analysis of characteristics of the variability in the peak latency, it is important to know to what extent the variability of the measured peak latency (measured variability) is actually caused by physiological factors (physiological variability). In our previous study, a method was developed for judging whether the physiological variability really exists or not, and if it does exist, the developed method extracts the physiological variability from the measured variability based on a limited number of single sweep records. In the present study, based on the P300 waveforms which were detected by blinded visual inspection of the ERP data obtained by an auditory oddball paradigm from 12 healthy adults, the physiological variability was shown to exist with a confidence level of 95% for all subjects. Furthermore, its interval estimate was calculated by subtracting noise variability from the measured variability with a confidence level of 80%, and it was found to range from 17 to 57 ms for all subjects.

Brief A morphological filter for extracting waveform characteristics of single-sweep evoked potentials

A morphological filter was designed for extracting the waveform characteristics of pain SEP from the single-sweep record. The properties of the basic operations for the morphological filter; erosion, dilation, opening and closing were clarified in order to design an appropriate filter. The morphological filter was designed by selecting the structuring elements, which represented the features of the pain SEP waveform. The designed morphological filter was evaluated using the simulation data, and applied to the pain SEP data obtained from a normal adult with satisfactory results.

Spectral properties of signal averaging and a novel technique for improving the signal-to-noise ratio.

Averaging is one well-known method for improving the signal-to-noise (S/N) ratio of data or a time series, in which small amplitude responses to regularly occurring stimuli are contaminated by noise. We have considered the spectral properties of that process and introduce a new method which further improves the S/N ratio. We also demonstrate that the signal recovered by normal averaging is identical to that recovered by the frequency extraction technique which we propose. A formula for the sums-of-squares of the recovered signal is derived. The success of the frequency extraction technique has been demonstrated on sinusoids, white noise, evoked potentials contaminated by a background EEG, and the EEG recorded with the subjects eyes closed.

Markov process amplitude EEG model for spontaneous background activity.

Summary: The Markov process amplitude (MPA) EEG model effectively representing spontaneous brain activity of the EEG was introduced. The relationship between the electrical mechanism for EEG generation and the proposed model was also investigated. The MPA EEG model was described by the sinusoidal waves with the randomly fluctuating amplitude of the first‐order Markov process. The parameters of the MPA EEG model were determined optimally based on the real EEG records. The results of model outputs in the frequency domain demonstrated an excellent fit with the power spectrum of the corresponding EEG. The simulated model signal in the time domain also showed good agreement with the EEG time series. The satisfactory results from the MPA EEG model suggest its possible applicability in clinical practice. Furthermore, from the high goodness of fit, the authors think that the neurons oscillate at fixed frequencies and are modulated by synaptic interactions in accordance with the first‐order Markov process.

Deterioration of average evoked potential waveform due to asynchronous averaging and its compensation

The deterioration of the average due to the asynchronous sampling of evoked potential data is discussed. The asynchronous averaging in the frequency domain is analyzed, and a relationship between the fluctuating time and the degree of the deterioration is derived. By using this relationship, a required sampling rate is derived to ensure the accuracy of the averaging. In the case of coarse sampling, a compensation procedure for the asynchronous averaging is deduced, by which the compensated signal approaches the original signal as the number of averagings increases.<<ETX>>

Construction of a morphological filter for detecting an event-related potential P300 in single sweep EEG record in children

A morphological filter for single sweep records of event-related potential (ERP) obtained in an auditory oddball paradigm, especially P300 waveform, was constructed. By combining four basic operations; erosion, dilation, opening and closing, we could derive a desired filter whose properties fit the current objectives. The morphological filter for the single sweep records of ERP was constructed by taking into account the features of the signal and noise components. The morphological filter had superior properties for distinguishing the signal from the noise even when both were within the same frequency band, as in case of children. The constructed morphological filter was evaluated by using the simulation data of ERP and then applied to the actual ERP data obtained from nine normal children. The constructed morphological filter was proved to be an appropriate tool for single sweep analysis of ERP.

Method for recording short latency evoked potentials using an EKG artifact elimination procedure

This paper describes the application of an EKG elimination procedure, previously reported by the authors, to evoked potentials (EPs) recording using a non-cephalic reference. The method consists of three separate steps: data acquisition, EKG artifact elimination, and EP averaging. EKG artifacts are eliminated from the raw EEG by applying a four step procedure to the simultaneously recorded EEG, EKG and stimulus pulse. The steps are: synchronized partition of the raw EEG, EKG averaging, synchronized repetition, and synchronized subtraction of the EKG estimate from the raw EEG. Average EP values are then obtained by averaging the processed EEG using the stimulus pulse as a trigger. Somatosensory evoked potentials to a hand reference, averaged using the proposed method, were compared with those obtained by two conventional averaging methods, and were shown to be more clearly defined. The advantage of the proposed method for recording short latency EP values with a non-cephalic reference is that it requires fewer sweeps and thus takes less time than other methods. The proposed method may also be applicable to the recording of other EP values.