Multiple channel detection of steady-state visual evoked potentials for brain-computer interfaces

In this paper, novel methods for detecting steady-state visual evoked potentials using multiple electroencephalogram (EEG) signals are presented. The methods are tailored for brain-computer interfacing, where fast and accurate detection is of vital importance for achieving high information transfer rates. High detection accuracy using short time segments is obtained by finding combinations of electrode signals that cancel strong interference signals in the EEG data. Data from a test group consisting of 10 subjects are used to evaluate the new methods and to compare them to standard techniques. Using 1-s signal segments, six different visual stimulation frequencies could be discriminated with an average classification accuracy of 84%. An additional advantage of the presented methodology is that it is fully online, i.e., no calibration data for noise estimation, feature extraction, or electrode selection is needed.     

A wavelet-based method for action potential detection from extracellular neural signal recording with low signal-to-noise ratio

We present a method for the detection of action potentials, an essential first step in the analysis of extracellular neural signals. The low signal-to-noise ratio (SNR) and similarity of spectral characteristic between the target signal and background noise are obstacles to solving this problem and, thus, in previous studies on experimental neurophysiology, only action potentials with sufficiently large amplitude have been detected and analyzed. In order to lower the level of SNR required for successful detection, we propose an action potential detector based on a prudent combination of wavelet coefficients of multiple scales and demonstrate its performance for neural signal recording with varying degrees of similarity between signal and noise. The experimental data include recordings from the rat somatosensory cortex, the giant medial nerve of crayfish, and the cutaneous nerve of bullfrog. The proposed method was tested for various SNR values and degrees of spectral similarity. The method was superior to the Teager energy operator and even comparable to or better than the optimal linear detector. A detection ratio higher than 80% at a false alarm ratio lower than 10% was achieved, under an SNR of 2.35 for the rat cortex data where the spectral similarity was very high.     

A statistical technique for measuring synchronism between cortical regions in the EEG during rhythmic stimulation

The coherence function has been widely applied in quantifying the degree of synchronism between electroencephalogram (EEG) signals obtained from different brain regions. However, when applied to investigating synchronization resulting from rhythmic stimulation, misleading results can arise from the high correlation of background EEG activity. We, thus propose a modified measure, which emphasizes the synchronized stimulus responses and reduces the influence of the spontaneous EEG activity. Critical values for this estimator are derived and tested in Monte Carlo simulations. The effectiveness of the method is illustrated on data recorded from 12 young normal subjects during rhythmic photic stimulation.     

An automatic sequential recognition method for cortical auditory evoked potentials

The detection of cortical auditory evoked potentials (CAEP), which are part of the electroencephalogram (EEG) in reaction to acoustic stimuli, has important applications such as determining objective audiograms. The detection is usually performed by a human operator, with support from often basic signal processing methods. This paper presents a novel mechanism for the detection of CAEPs, which is fully automatic and stops the measurement when a given confidence is reached. This proposed detector comprises of three stages. First, a feature extraction by a wavelet transform parameterizes the time domain EEG signal by only few transform coefficients. This feature vector is then classified by a neural network which yields a binary vote on every EEG segment. Finally, a sequential statistical test is performed on successive classifications; this stops the measurement if a specified decision confidence has been reached. The adjustment of the detector according to a clinical database is discussed. Thus adjusted, the proposed CAEP detection scheme is applied to a study, and compared with a human operator. The results demonstrate that this method can attain similar results, but outperforms the human expert for stimulation levels close to the hearing threshold.     

A low noise readout detector circuit for nanoampere sensorapplications

A readout detector IC has been developed which is capable of detecting nanoampere photo-current signals of interest in a high (microampere) background illumination or dc noise level (SNR=80 dB). The readout detector sensor IC processes transient signals of interest from a separate photo-diode pixel array chip. Low noise signal conditioning, filtering, and signal thresholding implement smart sensor detection of only “active pixels.” This detector circuit can also be used to perform signal conditioning for other sensor applications that require detection of very small signals in a high background noise environment     

差分探测器偏振光学相干层析信号分析

分析了使用差分探测器的偏振光学相干层析(PS-OCT)统噪声,讨论影响系统信噪比(SNR)的因素,模拟SNR随这些因素的变化趋势,根据分析得出实现最佳SNR的参数条件,为提高PS-OCT系统SNR提供了理论依据.相关实验验证了模拟结果的正确性.     

自适应编码调制的一种高效半盲检测算法

给出一种基于协议信息的自适应编码调制高效半盲检测算法,该算法同时采用信号多序列抽取和信号多频点滑动匹配两种方法,能够有效克服中频调制信号最佳采样点偏差和频率偏差的影响,可以准确高效地识别出信号类型、信道编码方式、调制类型。实验表明,该算法在低信噪比下就能达到很高的识别率,性能明显优于传统模式识别的检测算法,具有较好的实用前景。     

The optimum constant false alarm probability detector for relatively coherent multichannel signals in Gaussian noise of unknown power

In this paper, the method of "most powerful similar tests" is used to obtain the optimum (largest probability of detection) constant false alarm probability detector for multichannel signals received in the presence of additive Gaussian noise of unknown power. The signals are assumed to contain a common random phase angle, and hence are relatively coherent over the multiple channels. The noise is assumed to be correlated from channel to channel. The performance of the optimum detector is calculated. Finally, for illustrative purposes, the technique is applied to the detection of a signal in the presence of a jammer, and to the detection of a single channel signal in white and colored noise of unknown power.     

Spectrally efficient noncoherent communication

This paper considers noncoherent communication over a frequency-nonselective channel in which the time-varying channel gain is unknown a priori, but is approximately constant over a coherence interval. Unless the coherence interval is large, coherent communication, which requires explicit channel estimation and tracking prior to detection, incurs training overhead which may be excessive, especially for multiple-antenna communication. In contrast, noncoherent detection may be viewed as a generalized likelihood ratio test (GLRT) which jointly estimates the channel and the data, and hence does not require separate training. The main results in this paper are as follows. (1) We develop a "signal space" criterion for signal and code design for noncoherent communication, in terms of the distances of signal points from the decision boundaries. (2) The noncoherent metric thus obtained is used to guide the design of signals for noncoherent communication that are based on amplitude/phase constellations. These are significantly more efficient than conventional differential phase-shift keying (PSK), especially at high signal-to-noise ratio (SNR). Also, known results on the high-SNR performance of multiple-symbol demodulation of differential PSK are easily inferred from the noncoherent metric. (3) The GLRT interpretation is used to obtain near-optimal low-complexity implementations of noncoherent block demodulation. In particular, this gives an implementation of multiple symbol demodulation of differential PSK, which is of linear complexity (in the block length) and whose degradation from the exact, exponential complexity, implementation can be made as small as desired     

Approximate Time-Variable Coherence Analysis of Multichannel Signals

We present a new method for signal extraction from noisy multichannel epileptic seizure onset EEG signals. These signals are non-stationary which makes time-invariant filtering unsuitable. The new method assumes a signal model and performs denoising by filtering the signal of each channel using a time-variable filter which is an estimate of the Wiener filter. The approximate Wiener filters are obtained using the time-frequency coherence functions between all channel pairs, and a fix-point algorithm. We estimate the coherence functions using the multiple window method, after which the fix-point algorithm is applied. Simulations indicate that this method improves upon its restriction to assumed stationary signals for realistically non-stationary data, in terms of mean square error, and we show that it can also be used for time-frequency representation of noisy multichannel signals. The method was applied to two epileptic seizure onset signals, and it turned out that the most informative output of the method are the filters themselves studied in the time-frequency domain. They seem to reveal hidden features of the epileptic signal which are otherwise invisible. This algorithm can be used as preprocessing for seizure onset EEG signals prior to time-frequency representation and manual or algorithmic pattern classification.     

Demodulation of low-level broad-band optical signals with semiconductors

A simple approach to the problem of detection of optical signals with semiconductors leads to a general understanding of the performance. It shows that the low impedance level of broad-band circuits prevents high efficiency conversion of the incident power. Because of this, the envelope detector has a signal-to-noise ratio (SNR) which is determined by the noise of the following amplifier. As in radio communication, addition of a coherent optical signal from a local oscillator can increase SNR; however, for optical signals the limiting value of the noise figure is determined by the shot noise of the conversion current. Comparison with the limiting SNR expected from a photo-emission detector reveals no important distinction between them for frequency conversion. The unit which uses material with the higher quantum conversion efficiency will have the possibility of better SNR. For envelope detection, the photo emitter can be far superior to the photoconductive detector. The two cases of a photovoltaic and a photoelectromagnetic solid-state device are analyzed and general expressions as well as numerical examples given for the design parameters. The devices are comparable both in dimensions and performance.     

Conventional and wavelet coherence applied to sensory-evoked electrical brain activity

The use of coherence is a well-established standard approach for the analysis of biomedical signals. Being entirely based on frequency analysis, i.e., on spectral properties of the signal, it is not possible to obtain any information about the temporal structure of coherence which is useful in the study of brain dynamics, for example. Extending the concept of coherence as a measure of linear dependence between realizations of a random process to the wavelet transform, this paper introduces a new approach to coherence analysis which allows to monitor time-dependent changes in the coherence between electroenecphalographic (EEG) channels. Specifically, we analyzed multichannel EEG data of 26 subjects obtained in an experiment on associative learning, and compare the results of Fourier coherence and wavelet coherence, showing that wavelet coherence detects features that were inaccessible by application of Fourier coherence.     

Application of expectation-maximization algorithm to the detectionof a direct-sequence signal in pulsed noise jamming

The detection of a direct-sequence spread-spectrum signal received in a pulsed noise jamming environment is considered. The expectation-maximization algorithm is used to estimate the unknown jammer parameters and hence obtain a decision on the binary signal based on the estimated likelihood functions. The probability of error performance of the algorithm is simulated for a repeat code and a (7, 4) block code. Simulation results show that at low signal-to-thermal-noise ratio and high jammer power, the EM detector performs significantly better than the hard limiter and somewhat better than the soft limiter. Also, at low SNR, there is little degradation as compared to the maximum-likelihood detector with true jammer parameters. At high SNR, the soft limiter outperforms the EM detector     

A Time‐Varying Modified MMSE Detector for Multirate CDMA Signals in Fast Rayleigh Fading Channels

In this paper, we propose a time‐varying modified minimum mean‐squared error (MMSE) detector for the detection of higher data rate signals in a multirate asynchronous code‐division multiple‐access (CDMA) system which is signaled in a fast Rayleigh fading channel. The interference viewed by a higher data rate symbol will be periodic due to the presence of a lower data rate symbol which spans multiple higher data rate symbols. The detection is carried out on the basis of a modified MMSE criterion which incorporates differential detection and the ratio of channel coefficients in two consecutive observation intervals inherently compensating the fast variation of the channel due to fading. The numerical results obtained by the MMSE detector with time‐varying detection show around 3 dB (M=2) and 6 dB (M=4) performance improvement at a BER of 10^?3 in the AWGN channel, while introducing more computational complexity than the MMSE detector without time‐varying detection. At a higher Eb/N0, the proposed scheme can achieve a BER of approximately 10^?3 in the presence of fast channel variation which is an improvement over other schemes.     

Estimation of Postaverage SNR from Evoked Responses Under Nonstationary Noise

In any measure of event-related potentials, it is important to be able to estimate the postaverage signal-to-noise ratio (SNR) in order to assess the quality of the measured signals. The estimated postaverage SNR can be an important detection criteria (as in infant hearing-screening of evoked auditory potentials) and a control factor when comparing signals obtained during different conditions (accounting for residual noise variability). Standard SNR estimation methods, such as the fixed-single-point (Fsp) statistic (C. Elberling and M. Don, ldquoQuality estimation of averaged auditory brainstem responses,rdquo Scandinavian Audiol., vol. 13, pp. 187-197, 1984), assume a single-stationary noise source, with the postaverage SNR increasing proportionally to the number of trials averaged. This study proposes a modified version of the Fsp statistic, the nonstationary fixed-multiple-point (NS Fmp), that can account for a discrete number of noise sources of different power, and can also be modified for weighted averaging (WNS Fmp). A new noise segmentation procedure is also proposed that dynamically partitions contiguous trials based on their noise power estimates and a series of F-tests. Results from computer simulation and real data from auditory brain stem recordings show that the NS Fmp method yields lower mean square error than do the Fsp, and that the WNS Fmp has higher receiver-operating-curve area than do the standard Fsp procedure.     

基于小波降噪的压缩感知—循环平稳检测技术

认知无线电技术通过次级用户动态接入空闲频谱来提高空闲频谱资源的利用效率,是认知无线电的重要环节。在低信噪比环境下,如何快速精确地进行频谱感知是频谱感知面临的重大挑战。提出了一种基于小波降噪的压缩感知—循环平稳特征检测器来实现低信噪比环境下的频谱检测。采用压缩感知技术提高了频谱感知的效率,并进一步利用小波变换技术降低了压缩感知过程中引入的压缩噪声,提高了低信噪比环境下的频谱感知准确度。仿真结果证明,提出的基于小波降噪的压缩感知技术能够实现低信噪比环境下的频谱空洞检测。     

基于周期Choi-Williams Hough变换的线性调频连续波信号参数估计算法

在采用联合CWH(Choi-Williams Hough)变换估计多周期线性调频连续波(LFMCW)信号的参数时,当观察时间大于一个周期时,输出信号的信噪比不再随时间的增加而增大,且时频图中会出现多个峰值干扰信号参数的估计。结合CWH对LFMCW 信号的能量聚集和相干累积思想,该文提出一种基于周期CWH变换的多周期LFMCW信号的参数估计算法,给出了多周期LFMCW信号的PCWH变换公式;分析了PCWH输出信噪比与观测时间和观测样本信噪比的关系;分析了参数估计精度。最后,数值仿真验证了该算法的有效性,证明在对多周期LFMCW信号参数估计时,PCWH更优。     

Multiple access using two sets of orthogonal signal waveforms

We present a new multiple access concept which allows to accommodate more than N users on a channel of NW Hz bandwidth, where W Hz is the bandwidth of the individual user signals. It makes use of two sets of orthogonal signal waveforms, one for the first N users and one for the additional users. An iterative multistage detection technique is used to cancel interference between the two sets of users. At each stage of the detection process, the best estimate of the multiuser interference is synthesized using the decisions available from the previous stages, and this interference is subtracted from the user signals of interest before entering them to a threshold detector. The new concept is described using orthogonal code-division multiple access and time division multiple access as the two sets of signal waveforms, but we also briefly outline how it can be generalized to other orthogonal sets     

未知正弦信号的功率谱熵检测性能分析

低信噪比条件下无先验知识的信号检测问题是一类侦察问题。基于功率谱熵的分析算法是一种实际可行且有效的自动检测方法,但还没有建立相应的理论性能分析。针对白高斯噪声背景中检测未知正弦信号的问题描述,以统计理论分析功率谱熵检测的接收机工作特性曲线（ROC）,所得到的理论结果与仿真相比基本吻合。功率谱熵检测与能量检测的性能比较表明,功率谱熵检测性能明显优于后者。海试数据验证实际可检测的信噪比与理论预报相差不超过2 dB。      

一种基于散射中心密度的距离扩展目标检测方法

针对高分辨率雷达距离扩展目标的M/N检测器在目标空域散射中心密度较为稀疏时检测性能下降的问题,提出了一种基于散射中心密度的M/N(BSD.M/N)检测器.首先,推导出了检测器的平均虚警概率和检测概率表达式.其次,通过分析不同散射中心密度以及不同信杂比条件下检测器的检测性能与第二门限M取值的关系,给出了最佳M值的近似计算表达式.最后,仿真试验表明,这种检测器在散射中心稀疏情况下,检测性能要明显优于普通M/N检测器.