Acquisition Signal Processing Research Articles

Applications of Ultraviolet and Sub-ultraviolet Dermatoscopy in Neoplastic and Non-neoplastic Dermatoses: A Systematic Review.

Dermatoscopy is a non-invasive and cost-efficient imaging technique augmenting clinical examination in neoplastic and non-neoplastic dermatoses. Recently, novel dermatoscopic techniques based on principles of reflectance/absorption and excited fluorescence have been developed. However, comprehensive data on their applications are sparse, and terminology is inconsistent. In this systematic review, we addressed the principles of ultraviolet (UV) imaging and proposed categorization based on spectral characteristics and signal acquisition, as well as discussed documented and potential clinical applications, safety measures during examination, and limitations associated with reflectance and fluorescence dermatoscopy. A literature search was conducted in the PubMed medical database until 2December 2023 according to PRISMA guidelines, and 28 papers fit the scope of this review, whereas additional relevant articles were included to provide broader context regarding the chosen terminology, chromophores described, safety of sub-UV/UV, and regulations for light-emitting devices. UV and sub-UV dermatoscopy, categorized into different methods on the basis of the emitted wavelength and signal acquisition process (reflectance versus fluorescence), augment conventional dermatoscopy by optimizing safety margins in melanoma, facilitating early detection of tumor recurrence, and enhancing visualization in non-neoplastic conditions, including pigmentation disorders, intertrigo, papulo-desquamative dermatoses, and beyond. The review highlights the limitations of these techniques, including difficulty in differentiating melanin from hemoglobin, challenges in evaluating uneven surfaces, and artifacts. Although UV dermatoscopy complements conventional dermatoscopy, clinicians should be aware of their peculiarities, artifacts, limitations, and safety concerns to optimize their diagnostic accuracy and ensure patient's safety.

Microcontroller-Based Embedded System for the Diagnosis of Stator Winding Faults and Unbalanced Supply Voltage of the Induction Motors

Induction motors (IMs) are one of the most widely used motor types in the industry due to their low cost, high reliability, and efficiency. Nevertheless, like other types of AC motors, they are prone to various faults. In this article, a low-cost embedded system based on a microcontroller with the ARM Cortex-M4 core is proposed for the extraction of stator winding faults (interturn short circuits) and an unbalanced supply voltage of the induction motor drive. The voltage induced in the measurement coil by the axial flux was used as a source of diagnostic information. The process of signal measurement, acquisition, and processing using a cost-optimized embedded system (NUCLEO-L476RG), with the potential for industrial deployment, is described in detail. In addition, the analysis of the possibility of distinguishing between interturn short circuits and unbalanced supply voltage was carried out. The effect of motor operating conditions and fault severity on the symptom extraction process was also studied. The results of the experimental research conducted on a 1.5 kW IM confirmed the effectiveness of the developed embedded system in the extraction of these types of faults.

AN APPROACH ON THE DESCRIPTION OF A FLAT DRIVING BELT BEHAVIOUR MIRRORED IN TRANSMITTED MECHANICAL POWER

For driving belt condition monitoring, the main interest is the certification of the capacity to keep their qualities unchanged over a long period of time and secondary to detect the imminence of the catastrophic failure. This paper presents a study on the behaviour detection and description of a flat driving belt health condition, used in a rotary machine electrically driven, particularly a lathe headstock gearbox running idle. It was discovered that in the mechanical power transmitted from electromotor to gearbox via a flat belt some specific sinusoidal components (a fundamental and some harmonics) of variable power are generated. The description of these power components (by values of amplitude, frequency and phase at origin of time) is indirectly detectable in the evolution of the active electrical power absorbed by the drive electromotor. Two arguments are available for this approach. Firstly, there is a reasonable assumption that between the mechanical power and the active electrical power there is an approximated proportionality relationship through the power efficiency. Secondly, the evolution of the active electrical power (or mechanical power as well) is a deterministic signal with a low level of noise. A simple computer assisted procedure of active electrical power signal acquisition and data processing was conceived, the detection was done by computer aided curve-fitting procedures in Matlab applied on active electrical power evolution absorbed by the driving motor in stationary working regimes (the electromotor playing the role of a mechanical power sensor). Mainly two ways of graphic representation have been proposed in order to describe the variable power generated by this flat belt (in time and frequency domains). The behaviour of many other types of belts involved in rotary machines driving can be similarly described.

The performance of preliminary version of irregular heart rhythm notification algorithm of a consumer smartwatch for screening of atrial fibrillation

Abstract Background Smartwatch photoplethysmography (PPG) is a useful non-invasive tool for atrial fibrillation (AF) screening. However, the process of signal acquisition is vulnerable to variable factors such as motion, respiration, and unstable sensor contact which may lead to false positive results. Thus, an optimal signal acquisition and processing algorithm is needed to balance the inherent trade-off between sensitivity and specificity for smartwatch PPG to be used for AF screening in daily life. Purpose This study aimed to evaluate the efficacy of the preliminary version of the Irregular Heart Rhythm Notification (IHRN) feature of a consumer smartwatch for AF screening. Methods We obtained time-synchronized PPG and ECG signals using smartwatches and wearable ECG patches in 49 patients with paroxysmal and persistent AF in a free-living setting for up to 7 days. The IHRN algorithm selectively schedules PPG tachogram data acquisition at specific time interval, classifies heart rhythm for each interval, and integrates the classification results to determine whether to alert the user of their irregular heart rhythm. We tested the diagnostic performance of the IHRN algorithm for identifying AF episodes lasting 30 minutes or more. Results We obtained a total of 364,260 minutes of ECG recording and time-synchronized 27,524 minutes of PPG signal in 49 AF patients. Out of the PPG signals collected, 19,341 minutes (70.3%) were considered valid for the analysis. On the ECG analysis, 754 AF episodes (≥30 seconds) were identified. Longer-lasting AF episodes had a higher likelihood of being captured by PPG and consequently classified as AF by the algorithm (Figure 1). AF episodes lasting over 30 minutes were identified in 28 out of 49 patients. Regarding the subjective-level analysis, the sensitivity and specificity of IHRN for AF ≥ 30 minutes were 82.1% and 100.0%, respectively. In tachogram-level analysis, the positive predictive value was 98.7%. Conclusions The preliminary version of the IHRN algorithm of the smartwatch demonstrated excellent performance in identifying AF in a free-living setting. Further large-scale research is needed to determine whether this algorithm is useful for AF screening in a real-world setting.Figure 1

An In Situ Signal Amplification Sensor for Measuring In‐Plane Current Density Distribution in a Proton Exchange Membrane Fuel Cell

Inconsistency of current density distribution in‐plane in fuel cells is a common phenomenon. This study develops a novel highly integrated sensor with the feature of signal in situ amplification for measuring in‐plane current density. First, the sensor is designed and fabricated based on the idea of in situ amplification of in‐plane signals before transmission. Then, the sensor accuracy is quantitatively evaluated prior to fuel cell experiments based on a single‐point calibration approach. Finally, the distribution of in‐plane current density is observed using the sensor in the fuel cell test system. Additionally, the measurement accuracy of the circuit current in the test bench is compared by introducing the calibrated segment average amplification factors. The results indicate that these signals amplified by the sensor can effectively withstand the effects of signal transmission loss and interference, and it can be determined that the measurement accuracy of the sensor is controlled within 97% in the small current range and within 98% in the nonsmall current range in normal operation. This work provides a feasible solution for in situ measurements of in‐plane current density, with significant improvements in signal acquisition, structural matching, and fabrication process compared to existing such sensors.

Welding Penetration Monitoring for Ship Robotic GMAW Using Arc Sound Sensing Based on Improved Wavelet Denoising

The arc sound signal is one of the most important aspects of information related to pattern identification regarding the penetration state of ship robotic GMAW; however, arc sound is inevitably affected by noise interference during the signal acquisition process. In this paper, an improved wavelet threshold denoising method is proposed to eliminate interference and purify the arc sound signal. The non-stationary random distribution characteristics of GMAW noise interference are also estimated by using the high-frequency detail coefficients in different domains after wavelet transformation, and a mode of measuring scale that is logarithmically negatively correlated with the wavelet decomposition scale is created to update the threshold. The gradient convergent threshold function is established using the natural logarithmic function structure and concave–convex gradient to enable the nonlinear adjustment of the asymptotic rate. Further, some property theorems related to the optimized threshold function are proposed and theoretically proven, and the effectiveness and adaptability of the improved method are verified via the denoising simulation of speech synthesis signals. The four traditional denoising methods and our improved version are applied in the pretreatment of the GMAW arc sound signal, respectively. Statistical analysis and short-time Fourier transform are used to extract eight-dimensional time and frequency domain feature parameters from the denoised signals with randomly time-varying characteristics, and the extracted joint feature parameters are used to establish a nonlinear mapping model of penetration state identification for ship robotic GMAW using the pattern classifiers of RBFNN, PNN and PSO-SVM. The simulation results yielded by visual penetration classification and the multi-dimensional evaluation index of the confusion matrix indicate that the improved denoising method proposed in this paper achieves a higher accuracy in the extraction of penetration state features and greater precision in the identification of pattern classification.

Research on fault diagnosis method of electric gate valve under strong background noise

Electric gate valves are essential equipment in nuclear power plants. The safe and stable operation of an electric gate valve is key in maintaining the safety of nuclear power plant. However, due to the strong surrounding noise during the signal acquisition process that affect nuclear power plant electric gate valves, the early status monitoring and fault diagnosis of the electric gate valve are ineffective. However, the traditional fault diagnosis methods are not suitable for electric gate valves and fail to classify faults classification under the strong background noise. To achieve the noise reduction of electric gate valve acceleration signals based on strong background noise, the work proposes a Variational Mode Decomposition (VMD) method to denoise the strong noise in the collected signal. Seven genetic optimization algorithms are combined with VMD to enhance the model parameter selection of VMD. This selection criterion helps to select the most suitable algorithm combination for processing the acceleration signal of the electric gate valve under the background of strong noise. In order to obtain a better fault classification effect, the weak noise in the acceleration signal is further removed by Singular Value Decomposition (SVD). Moreover, the time-domain feature parameter extraction is performed on the denoised acceleration signal. Consequently, the model training of the Bidirectional Gate Recurrent Unit (BGRU) and the development of the electric gate valve fault diagnosis system under the background of strong noise are completed. The final experimental results show that the system developed in this paper has a good fault classification effect.

Hybrid Threshold Denoising Framework Using Singular Value Decomposition for Side-Channel Analysis Preprocessing.

The traces used in side-channel analysis are essential to breaking the key of encryption and the signal quality greatly affects the correct rate of key guessing. Therefore, the preprocessing of side-channel traces plays an important role in side-channel analysis. The process of side-channel leakage signal acquisition is usually affected by internal circuit noise, external environmental noise, and other factors, so the collected signal is often mixed with strong noise. In order to extract the feature information of side-channel signals from very low signal-to-noise ratio traces, a hybrid threshold denoising framework using singular value decomposition is proposed for side-channel analysis preprocessing. This framework is based on singular value decomposition and introduces low-rank matrix approximation theory to improve the rank selection methods of singular value decomposition. This paper combines the hard threshold method of truncated singular value decomposition with the soft threshold method of singular value shrinkage damping and proposes a hybrid threshold denoising framework using singular value decomposition for the data preprocessing step of side-channel analysis as a general preprocessing method for non-profiled side-channel analysis. The data used in the experimental evaluation are from the raw traces of the public database of DPA contest V2 and AES_HD. The success rate curve of non-profiled side-channel analysis further confirms the effectiveness of the proposed framework. Moreover, the signal-to-noise ratio of traces is significantly improved after preprocessing, and the correlation with the correct key is also significantly enhanced. Experimental results on DPA v2 and AES_HD show that the proposed noise reduction framework can be effectively applied to the side-channel analysis preprocessing step, and can successfully improve the signal-to-noise ratio of the traces and the attack efficiency.

QTT Ultra-wideband Signal Acquisition and Baseband Data Recording System Design Based on the RFSoC Platform

Abstract The 110 m QiTai radio Telescope (QTT) will be equipped with multiple Ultra-WideBand (UWB) receivers in the primary and Gregory focus to achieve continuous frequency coverage from 270 MHz to 115 GHz, which poses great challenges to signal acquisition, transmission, and real-time processing. Aiming at 10 GHz and above full-bandwidth acquisition and multi-scientific processing for the QTT UWB signals, an experimental system with high-speed signal acquisition, 100 Gb network multi-path distribution, and fast recording is designed by using advanced direct RF-sampling technology and heterogeneous architecture. The system employs a ZCU111 board to digitize dual-polarization signals with a sampling rate of 4.096 GigaSamples-Per-Second and 12-bit quantization. The collected wideband signals are channelized into 2048 chunks, which are then assembled into 16 sets of digital narrow basebands with 128 MHz bandwidth and transmitted to the processing servers through two 100 Gb ports. A HASPIPE pipeline, UWB_HASHPIPE is designed to receive and store multiple subbands in parallel. Data distribution links can be flexibly configured based on IP addresses and port numbers. The system is verified by pulsar observation experiments on the Nanshan 26 m telescope. 512 MHz bandwidth is selected from the collected L-band receiver signals and recorded in VDIF file format with 8 parallel instances. The test results show that the data integrity is excellent, and the signal-to-noise ratio of the band-merged pulsar profile is stronger than single subband data. This paper provides a high-performance and flexible solution for the design of versatile UWB backends. Meanwhile, the developed platform can be integrated into QTT backends for baseband data collection and Very Long Baseline Interferometry observation.

An algorithm for obtaining the frequency and the times of respiratory phases from nasal and oral acoustic signals

<span lang="EN-US">This work proposes a computational algorithm which extracts the frequency, timings and signal segments corresponding to respiratory phases, through buccal and nasal acoustic signal processing. The proposal offers a computational solution for medical applications which require on-site or remote patient monitoring and evaluation of pulmonary pathologies, such as coronavirus disease 19 (COVID-19). The state of the art presents a few respiratory evaluation proposals through buccal and nasal acoustic signals. Most proposals focus on respiratory signals acquired by a medical professional, using stethoscopes and electrodes located on the thorax. In this case the signal acquisition process is carried out through the use of a low cost and easy to use mask, which is equipped with strategically positioned and connected electret microphones, to maximize the proposed algorithm’s performance. The algorithm employs signal processing techniques such as signal envelope detection, decimation, fast Fourier transform (FFT) and detection of peaks and time intervals via estimation of local maxima and minima in a signal’s envelope. For the validation process a database of 32 signals of different respiratory modes and frequencies was used. Results show a maximum average error of 2.23% for breathing rate, 2.81% for expiration time and 3.47% for inspiration time.</span>

Visual Electrochemiluminescence Biosensor Chip Based on Distance Readout for Deoxynivalenol Detection

Visual electrochemiluminescence (ECL) biosensors do not need complex instruments or well-trained operators, which is regarded as an ideal choice for portable and low-cost detection. But traditional visual ECL biosensors are based on the change in ECL intensity, which is easily affected by environmental factors and signal acquisition processes. In this work, a visual ECL biosensor chip based on distance readout has been developed for the first time. The chip is composed of a square detection region and a visual channel region, which are modified with graphene oxide (GO) and gold nanoparticles (AuNPs)@Ti3C2 nanocomposites, respectively. Target molecules can release aptamers adsorbed on the GO surface of the detection region and further change the electrode potential of the visual channel region, which can determine the length of the long channel that generates visible ECL signals. The application of AuNPs@Ti3C2 nanocomposites can effectively enhance ECL intensity by six times. Through the unique design of the sensor chip, quantification detection can be achieved based on the length change instead of the traditional intensity change. This visual ECL sensor is successfully applied for deoxynivalenol toxin detection in actual samples, demonstrating that the introduction of the distance readout strategy into ECL sensing has a good prospect in on-site testing.

Educational trends in biomedical signal processing

Abstract Biomedical Engineering programs are present at many universities all over the world with an increasing trend. New generations of biomedical engineers have to face the challenges of health care systems round the world which need a large number of professionals not only to support the present technology in the health care system but to develop new devices and services. Biomedical Engineering supports patient diagnosis and treatment by installing, testing, calibrating and repairing biomedical equipment; training users; maintaining safe operations. Also, approves new equipment by conducting tests, ensuring adherence to codes and making modifications. This paper presents a solution for biomedical signal processing education, involving real signal acquisition and computer-based processing. The acquired signals also allow a lot of post processing procedures to be implemented in an educational form/way.

High-Performance Compact Pre-Lens Retarding Field Energy Analyzer for Energy Distribution Measurements of an Electron Gun.

The energy distribution of an electron gun is one of the most important characteristics determining the performance of electron beam-based instruments, such as electron microscopes and electron energy loss spectroscopes. For accurate measurements of the energy distribution, this study presents a novel retarding field energy analyzer (RFEA) with the feature of an additional integrated pre-lens, which enables an adjustment of beam trajectory into the analyzer. The advantages of this analyzer are its compact size and simple electrode configuration. According to trajectory simulation theories, the optimum condition arises when the incident electron beam inside the RFEA is focused on the center of a retarding electrode. Comparing I–V curves depending on whether the pre-lens working or not, it is confirmed that the use of the pre-lens dramatically improves the energy resolution and efficiency of the signal acquisition process. The pre-lens RFEA was applied to characterize a Schottky electron gun under various temperatures and extraction voltages as operational conditions. When the tip temperature was increased by 50 K, we were able to measure an energy distribution broadening of 13.8 meV with the proposed pre-lens RFEA. The relative standard deviation of energy distribution was 0.7% for each working condition.

GNSS Multiparameter Spoofing Detection Method Based on Support Vector Machine

The vulnerability of the global navigation satellite system (GNSS) to spoofing limits its vast application in military safety and the national economy. Therefore, it is significantly important to rapidly and accurately detect GNSS spoofing. The traditional spoofing detection mainly detects spoofing signal intrusion by individual parameters such as signal power and signal quality. The single-parameter detection method can no longer manage complex and changing scenarios with the continuous development of spoofing technology. This paper proposes a GNSS multi-parameter joint detection method based on the support vector machine (SVM). The effects of spoofing on the signal acquisition, signal tracking, and position, velocity and time (PVT) solution processes are analyzed, and the composite signal quality monitoring (SQM), carrier-to-noise ratio C/N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> , pseudo-range Doppler consistency, PVT solving residuals, and clock difference and clock drift are extracted as detection features for training and testing of the support vector machine binary classification model. The F1-score of all nine-types parameter detection methods on TEXBAT and OAKBAT datasets improved by 22.95% and 19.6% (reach 93.97% and 97%), respectively, compared with only SQM parameters selected method. The obtained results demonstrate significant improvement in the spoofing detection performance compared with the traditional single-parameter methods.

The design and realization of flexible wearable wireless music controller

&lt;p&gt;By using flexible sensors and micro-control units, a wireless controller that can be integrated into clothing for cross-platform music control has been designed and implemented, providing a new idea for making a simple and low-cost flexible wearable sensing system. The design and implementation of a wireless controller that can be integrated into clothing for cross-platform music control provides a new idea for the preparation of a simple and low-cost flexible wearable sensing system. Based on the flexible fabric sensing material, a simple structured sensor piece is proposed as a button for the controller with good wearing comfort. The sensor element is capable of sensing finger presses up to 15kPa. ESP32 is used as a micro-control unit for sensing signal acquisition and data processing. Using the Bluetooth chip integrated inside the ESP32, the controller can be connected with terminal devices of different platforms for wireless data transmission. The results show that the prepared wireless music controller can be stably connected with both Windows computer terminal and Android cell phone terminal, and the sensor recognition accuracy of finger press is 99.7%, which indicates that the flexible fabric sensor has a broad application prospect in the field of wearable devices.&lt;/p&gt;

Research on an ID-PCA Early Fault Detection Method for Rolling Bearings

Since the rolling bearing is complex during the signal acquisition process, there is a certain loss during the process of collecting the vibration signal. This has led to the weakness of the early fault characteristics of the rolling bearing, affecting the accuracy of the rolling bearing fault feature extraction. In response to the above problems, an early fault detection method based on the Improved Deep Principal Component Analysis (ID-PCA) is proposed. The proposed method uses the time-series characteristic information of the vibration signal to establish a model, which solves the problem that the principal component analysis method cannot detect the vibration signal directly. Through the deep decomposition theorem, a multi-layer data processing model is established to fully mine the weak fault features in the vibration signal. It can solve the problem of inaccurate early fault detection results due to weak fault feature information. The reliability of this method is proved theoretically through sensitivity analysis. Finally, through experimental simulation, the accuracy and feasibility of this method are proved from the perspective of practice.

A Three-Dimensional-Printed Recyclable, Flexible, and Wearable Device for Visualized UV, Temperature, and Sweat pH Sensing.

Wearable devices are now recognized as a powerful tool to collect physiological and environmental information in a smart, noninvasive, and real-time manner. Despite the rapid progress of wearable devices especially wearable electronic devices, there are still several challenges that limit their further development, for example, a complicated electrical signal acquisition and processing process to eliminate the interference from the surrounding signals, bulky power supply, inevitable e-waste, and environmental pollution. Herein, we report a 3D-printed recyclable, flexible, and wearable device for visualized UV, temperature, and sweat pH sensing. Compared with wearable electronic devices, our visualized wearable device senses environmental (UV light, ambient temperature), biophysical (skin temperature), and biochemical (sweat pH) signals via stimuli-responsive color change, which does not require complicated electronic circuit design/assembly, time-consuming data processing and additional power source. In addition, this visualized wearable device is fabricated via a 3D support bath printing technology by printing UV-, temperature-, and sweat pH-sensing inks containing photochromic, thermochromic, and pH-chromic materials, respectively, into/onto sustainable starch solution, resulting in a multi-functional, recyclable, and flexible sensing device with high reproducibility. Our results reveal that UV light intensities under sunlight (0–2500 μW/cm2), ambient, and skin temperatures (0–38 °C) as well as sweat pH (4.0–7.0) can be successfully monitored.

Conversion of bioelectric sEMG signals into analog form for the BLDC motors control

This paper presents the process of converting the bioelectrical sEMG signal into an analog signal, which is useful from the point of view of motor or actuator control. The constructed measurement path for signal acquisition enables examining the "raw" signal from sEMG sensors and tracing the entire path of signal processing up to its "useful" form, i.e., the analog signal reflecting the degree of contraction of a selected muscle. This paper presents the successive signal processing and filtering stages and discusses the electrical circuits used in the signal acquisition process.

A High-Sensitivity and Shielding-Free Rabbit Brain MIPS Scanner

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Goal:</i> The purpose of this paper is to improve the detection sensitivity and quantitative assessment of different levels of cerebral hemorrhage (CH), a high-sensitivity and shielding-free rabbit brain magnetic induction phase shift (MIPS) scanner was developed and validated. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methods:</i> First, a uniform rotating magnetic field generated by a birdcage coil was utilized instead of the divergent magnetic field for the first time, and the birdcage structure has inherent characteristics of shielding external interference. Furthermore, the primary field was cancelled, and the axial and tangential magnetic field components of the secondary magnetic field were synchronously detected and then synthesized for the first time using a magnetron sensor coil and a circular solenoid coil. Specifically, a field programmable gate array-based spectrometer was used to guarantee the two-channel signal of high-precision quadrature excitation for birdcage coil, signal acquisition, and data processing. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> Experimental results indicated that the phase shift difference decreases linearly with the volume increase of blood injection, and the average MIPS caused by a 3-ml blood injection of rabbit inner capsule hemorrhage was 2.709°, which was 7.7 times higher than that of the traditional single excitation coil and single receiving coil (single coil–coil) method. The results have proven that this scanner has high sensitivity, strong anti-interference ability, and potential to realize the quantitative detection of hematoma volume.

EEG based Drowsiness Prediction Using Machine Learning Approach

Drowsiness is the main cause of road accidents and it leads to severe physical injury, death, and significant economic losses. To monitor driver drowsiness various methods like Behaviour measures, Vehicle measures, Physiological measures and Hybrid measures have been used in previous research. This paper mainly focuses on physiological methods to predict the driver’s drowsiness. Several physiological methods are used to predict drowsiness. Among those methods, Electroencephalography is one of the non-invasive physiological methods to measure the brain activity of the subject. EEG brain signal extracted from the human scalp is analysed with various features and used for various health application like predicting drowsiness, fatigue etc. The main objective of the proposed system is to early predict the driver drowsiness with high accuracy so that we have divided our work into two steps. The first step is to collect the publicly available dataset of EEG based Eye state as (Eye open and Eye closed) where the signal acquisition process was done from Emotiv EEG Neuroheadset (14 electrodes) and analysed various feature engineering techniques and statistical techniques. The second step was applied with the machine learning classification model as K-NN and performance-based predicting models are used. In the Existing System, they used various machine learning classification models like K-NN and SVM for EEG Eye state classification and produced results around 80% -97%. Compared to the Existing system our proposed method produced better classification models for predicting driver drowsiness using different Feature engineering process and classification models as K-NN produced 98% of accuracy.