With the continuing growth of voice-controlled devices, voice metrics have been widely used for user identification. However, voice biometrics is vulnerable to replay attacks and ambient noise. We identify that the fundamental vulnerability in voice biometrics is rooted in its indirect sensing modality (e.g., microphone). In this paper, we presentVocalPrint, a resilient mmWave interrogation system which directly captures and analyzes the vocal vibrations for user authentication. Specifically,VocalPrintexploits the unique disturbance of the skin-reflect radio frequency (RF) signals around the near-throat region of the user, caused by the vocal vibrations. The complex ambient noise is isolated from the RF signal using a novel resilience-aware clutter suppression approach for preserving fine-grained vocal biometric properties. Afterward, we extract the vocal tract and vocal source features and input them into an ensemble classifier for authentication.VocalPrintis practical as it allows the effortless transition to a smartphone while having sufficient usability due to its non-contact nature. Our experimental results from 41 participants with different interrogation distances, orientations, and body motions show thatVocalPrintachieves over 96 percent authentication accuracy even under unfavorable conditions. We demonstrate the resilience of our system against complex noise interference and spoof attacks of various threat levels.

Drug effectiveness management is a complicated and challenging task in chronic diseases, like Parkinson's Disease (PD). Drug effectiveness control is not only linked to personal out-of-pocket cost but also affecting the quality of life among patients with chronic symptoms. In the current practice, although that health and medical professionals still play a key role in the personalized treatment plan, the critical decision on drug selection falls upon the individual report when patients call in or visit the clinics. Unfortunately, most of the patients with chronic diseases either fail to report their day-to-day symptoms or have a limited access to medical resources due to economic constraints. In this paper, we present PDLens, a first smartphone-based system to detect drug effectiveness among Parkinson's in daily life. Specifically, PDLens can extract digital behavioral markers related to PD drug responses from everyday activities, including phone calls, standing, and walking. PDLens models the PD symptom severity on drug treatment and detects the change of severity scores before and after drug intake. A ranking-based multi-view deep neural network is developed to decide the drug effectiveness upon the symptom severity changes. To validate the performance of PDLens, we conduct a pilot study with 81 PD patients and monitor their smartphone activities and severity changes over 33693 drug intake events across six (6) months. Compared with the standard clinical drug effectiveness test developed by Motor Disorder Society, results reveal that PDLens is a promising tool to facilitate drug effectiveness detection among PD patients in their daily lives.

Approximately 7 million survivors of stroke reside in the United States. Over half of these individuals will have residual deficits, making stroke one of the leading causes of disability. Long-term rehabilitation opportunities are critical for millions of individuals with chronic upper limb motor deicits due to stroke. Traditional in-home rehabilitation is reported to be dull, boring, and un-engaging. Moreover, existing rehabilitation technologies are not user-friendly and cannot be adaptable to different and ever-changing demands from individual stroke survivors. In this work, we present RehabPhone, a highly-usable software-defined stroke rehabilitation paradigm using the smartphone and 3D printing technologies. This software-definition has twofold. First, RehabPhone leverages the cost-effective 3D printing technology to augment ordinal smartphones into customized rehabilitation tools. The size, weight, and shape of rehabilitation tools are software-defined according to individual rehabilitation needs and goals. Second, RehabPhone integrates 13 functional rehabilitation activities co-designed with stroke professionals into a smartphone APP. The software utilizes built-in smartphone sensors to analyzes rehabilitation activities and provides real-time feedback to coach and engage stroke users. We perform the in-lab usability optimization with the RehabPhone prototype with involving 16 healthy adults and 4 stroke survivors. After that, we conduct a 6-week unattended intervention study in 12 homes of stroke residence. In the course of the clinical study, over 32,000 samples of physical rehabilitation activities are collected and evaluated. Results indicate that stroke users with RehabPhone demonstrate a high adherence and clinical efficacy in a self-managed home-based rehabilitation course. To the best of our knowledge, this is the first exploratory clinical study using mobile health technologies in real-world stroke rehabilitation.

With the continuing growth of voice-controlled devices, voice metrics have been widely used for user identification. However, voice biometrics is vulnerable to replay attacks and ambient noise. We identify that the fundamental vulnerability in voice biometrics is rooted in its indirect sensing modality (e.g., microphone). In this paper, we present VocalPrint, a resilient mmWave interrogation system which directly captures and analyzes the vocal vibrations for user authentication. Specifically, VocalPrint exploits the unique disturbance of the skin-reflect radio frequency (RF) signals around the near-throat region of the user, caused by the vocal vibrations during communication. The complex ambient noise is isolated from the RF signal using a novel resilience-aware clutter suppression approach for preserving fine-grained vocal biometric properties. Afterward, we extract the text-independent vocal tract and vocal source features and input them to an ensemble classifier for user authentication. VocalPrint is practical as it leverages a low-cost, portable, and energy-efficient hardware allowing effortless transition to a smartphone while having sufficient usability as typical voice authentication systems due to its non-contact nature. Our experimental results from 41 participants with different interrogation distances, orientations, and body motions show that VocalPrint can achieve over 96% authentication accuracy even under unfavorable conditions. We demonstrate the resilience of our system against complex noise interference and spoof attacks of various threat levels.

Biometric authentication offers advantages over current security practices. Unlike keys and tokens, biometrics are never lost or stolen. Unlike passwords, biometrics cannot be forgotten. However, existing biometric systems are with controversy: once divulged, they are compromised forever. To this end, this paper explores a truly cancelable brain-based biometric system for the first time. Specifically, we present a new psychophysiological protocol via non-volitional brain response for trustworthy user authentication, with an application example of smart headwear. More specifically, we address the following research challenges in a theoretical and experimental combined manner: (1) how to generate reliable brain responses with sophisticated visual stimuli; (2) how to acquire effective brain response and analyze unique features in them for authentication; and (3) how to reset and change brain biometrics when the current biometric credential is divulged. To evaluate the performance of the proposed system, we conducted a pilot study and achieved an f-score accuracy of 95.46 percent and equal error rate (EER) of 2.503 percent, thereby demonstrating the potential feasibility of neurofeedback based biometrics for smart headwear applications. Further, the cancelability study proves the effectiveness of the reset brain password. To the best of our knowledge, it is the first in-depth research study on truly cancelable brain biometrics.

The ill-fitting shoes can cause many health implications, ranging from feet ache to feet sore, from back injury to back pain and so on. Due to the manufacturing variance and design consideration, shoes from different brands will vary in size and shape. Good-fit insoles and shoes are necessary to prevent users from ill-fitting problems. Hence, we propose a novel technique that can be used to calculate parameters of the foot from smartphone RGB camera inputs. Typically, we focus on estimating six main parameters of the foot, including foot length, foot width, foot back height, toe height, heel circumference and foot circumference. These six parameters will significantly help users to find the right shoes or order a tailor-made insoles that fits their foot condition so that they can sustain their foot health and avoid further damage. Therefore, we applied different mathematical formulas accordingly and designed algorithms to estimate foot parameters.

Parkinson's disease (PD) is a chronic neurodegenerative disorder resulting from the progressive loss of dopaminergic nerve cells. People with PD usually demonstrate deficits in performing basic daily activities, and the relevant annual social cost can reach about $25 billion in the United States. Early detection of PD plays an important role in symptom relief and improvement in the performance of activities in daily life (ADL), which eventually reduces societal and economic burden. However, conventional PD detection methods are inconvenient in daily life (e.g., requiring users to wear sensors). To overcome this challenge, we propose and identify the non-speech body sounds as the new PD biomarker, and utilize the data in smartphone usage to realize the passive PD detection in daily life without interrupting the user. Specifically, we present PDVocal, an end-to-end smartphone-based privacy-preserving system towards early PD detection. PDVocal can passively recognize the PD digital biomarkers in the voice data during daily phone conversation. At the user end, PDVocal filters the audio stream and only extracts the non-speech body sounds (e.g., breathing, clearing throat and swallowing) which contain no privacy-sensitive content. At the cloud end, PDVocal analyzes the body sounds of interest and assesses the health condition using a customized residual network. For the sake of reliability in real-world PD detection, we investigate the method of the performance optimizer including an opportunistic learning knob and a long-term tracking protocol. We evaluate our proposed PDVocal on a collected dataset from 890 participants and real-life conversations from publicly available data sources. Results indicate that non-speech body sounds are a promising digital biomarker for privacy-preserving PD detection in daily life.

Digital devices have become a necessity in our daily life, with digital screens acting as a gateway to access a plethora of information present in the underlying device. However, these devices emit visible light through screens where long-term use can lead to significant screen exposure, further influencing users' health. Conventional methods on screen exposure detection (\textite.g., photo logger) are usually privacy-invasive and expensive, further, require ideal light conditions, which are unattainable in real practice. Considering the light intensity and spectrum vary among different light sources, an effective screen spectrum estimation can provide vital information about screen exposure. To this end, we first investigate the characteristics of the junction between p-type and n-type semiconductor (i.e., PN junction) to sense the spectrum under various conditions. Empirically, we design and implement, \textsfSpecEye, an end-to-end, low cost, wearable, and privacy-preserving screen exposure detection system with a mobile application. For validating the performance of our system, we conduct comprehensive experiments with $54$ commodity digital screens, at $43$ distinct locations, with results showing a base accuracy of $99$%, and an equal error rate (EER) approaching $0.80$% under the controlled lab setup. Moreover, we assess the reliability, robustness, and performance variation of \textsfSpecEye under various real-world circumstances to observe a stable accuracy of $95$%. Our real-world study indicates \textsfSpecEye is a promising system for screen exposure detection in everyday life.

Voice-user interface (VUI) has become an integral component in modern personal devices (\textite.g., smartphones, voice assistant) by fundamentally evolving the information sharing between the user and device. Acoustic sensing for VUI is designed to sense all acoustic objects; however, the existing VUI mechanism can only offer low-quality speech sensing. This is due to the audible and inaudible interference from complex ambient noise that limits the performance of VUI by causing denial-of-service (DoS) of user requests. Therefore, it is of paramount importance to enable noise-resistant speech sensing in VUI for executing critical tasks with superior efficiency and precision in robust environments. To this end, we investigate the feasibility of employing radio-frequency signals, such as millimeter wave (mmWave) for sensing the noise-resistant voice of an individual. We first perform an in-depth study behind the rationale of voice generation and resulting vocal vibrations. From the obtained insights, we presentWaveEar, an end-to-end noise-resistant speech sensing system.WaveEar comprises a low-cost mmWave probe to localize the position of the speaker among multiple people and direct the mmWave signals towards the near-throat region of the speaker for sensing his/her vocal vibrations. The received signal, containing the speech information, is fed to our novel deep neural network for recovering the voice through exhaustive extraction. Our experimental evaluation under real-world scenarios with 21 participants shows the effectiveness ofWaveEar to precisely infer the noise-resistant voice and enable a pervasive VUI in modern electronic devices.

Continuous authentication is superior to conventional one-pass authentication by maintaining the security level of a system throughout the entire login session. Leveraging the unique geometric and non-volitional credentials in the cardiac motion, we present a trustworthy, continuous, and non-contact user authentication system. Based on a pilot study with 78 subjects, we evaluate Cardiac Scan in terms of accuracy, authentication time, permanence, and vulnerability. The results show that Cardiac Scan is a robust and usable continuous authentication system.

With the growth of the elder population, fall risk evaluation is crucial to prevent elders from serious injuries, as well as reduce related financial burdens. A balance assessment, timed up and go (TUG), has been widely used to estimate fall risk. The standardized TUG focuses on flat ground walking with no environmental variance. Therefore, it falls short of assessing an individual's gait adaptability. Being able to adjust steps in response to environmental changes, for example, needing to navigate around or over a child's toy left on the sidewalk, is essential to avoid fall risk and fundamental to community ambulation. To this end, we propose four environment-adapting TUGs designed to assess one's ability to adapt gait in complex environments and a compatible system named Smart Insole TUG (SITUG), which provides real-time, feature-rich, and ease-of-operation TUG analysis. Based on experimental results, SITUG is capable of extracting gait related spatial-temporal features with all mean accuracies over 92%. Besides, the system achieves a mean accuracy of 92.23% in segmenting five TUG phases.

Motor learning is the ability to acquire a new motor skill, which plays an important role in rehabilitation as patients learn exercise programs or modify movements to regain pain free function. In this paper, we design an easy-to-use multimodal sensor system to assess motor learning. We developed a motor learning assessment device with a touch screen and Leap Motion to record the subject hand movement during a Serial Reaction Time Task(SRTT). The SRTT consists of upper limb reaching to targets in multi-dimensions. The device records metrics of time and movement efficiency and examines motor learning based on data analysis. This device can provide clinicians with data that can inform their approach to training. We recruited a total of 11 participants, with and without chronic pain to evaluate the device using a classifier model to assess participants' performance. The model shows our system works well to identify motor learning differences in individuals with and without chronic pain.

Motor impairment is common following stroke. Diminished strength and coordination contribute to reduced ability to perform activities of daily living. The existing healthcare models focus on delivering rehabilitation during the first few months following stroke. Yet, to regain motor control to the greatest degree, rehabilitation should continue across the lifespan. Currently, individuals with stroke are responsible for self-managing their rehabilitation once therapist guided rehabilitation has concluded. Individuals with stroke are frequently given a written home exercise program to help guide their home rehabilitation, but poor compliance demonstrates a better approach is necessary. In this study, we propose BiGRA, a novel system to more effectively facilitate in-home bilateral rehabilitation. This system holds merits in: (1) An end-to-end task-oriented system for bilateral grip control which emphasizes the modulation of grip coordination between hands; and (2) Innovative metrics framework to quantitatively analyze the motor control performance. The evaluation shows that BiGRA can objectively measure the patients' task performance and is a promising assessment tool for stroke rehabilitation.

As 3D printing technology begins to outpace traditional manufacturing, malicious users increasingly have sought to leverage this widely accessible platform to produce unlawful tools for criminal activities. Therefore, it is of paramount importance to identify the origin of unlawful 3D printed products using digital forensics. Traditional countermeasures, including information embedding or watermarking, rely on supervised manufacturing process and are impractical for identifying the origin of 3D printed tools in criminal applications. We argue that 3D printers possess unique fingerprints, which arise from hardware imperfections during the manufacturing process, causing discrepancies in the line formation of printed physical objects. These variations appear repeatedly and result in unique textures that can serve as a viable fingerprint on associated 3D printed products. To address the challenge of traditional forensics in identifying unlawful 3D printed products, we present PrinTracker, the 3D printer identification system, which can precisely trace the physical object to its source 3D printer based on their fingerprint. Results indicate that PrinTracker provides a high accuracy using 14 different 3D printers. Under unfavorable conditions (e.g. restricted sample area, location and process), the PrinTracker can still achieve an acceptable accuracy of 92%. Furthermore, we examine the effectiveness, robustness, reliability and vulnerabilities of the PrinTracker in multiple real-world scenarios.

Driven by a wide range of real-world applications, significant efforts have recently been made to explore device-free human activity recognition techniques that utilize the information collected by various wireless infrastructures to infer human activities without the need for the monitored subject to carry a dedicated device. Existing device free human activity recognition approaches and systems, though yielding reasonably good performance in certain cases, are faced with a major challenge. The wireless signals arriving at the receiving devices usually carry substantial information that is specific to the environment where the activities are recorded and the human subject who conducts the activities. Due to this reason, an activity recognition model that is trained on a specific subject in a specific environment typically does not work well when being applied to predict another subject's activities that are recorded in a different environment. To address this challenge, in this paper, we propose EI, a deep-learning based device free activity recognition framework that can remove the environment and subject specific information contained in the activity data and extract environment/subject-independent features shared by the data collected on different subjects under different environments. We conduct extensive experiments on four different device free activity recognition testbeds: WiFi, ultrasound, 60 GHz mmWave, and visible light. The experimental results demonstrate the superior effectiveness and generalizability of the proposed EI framework.

In recent years, biometric techniques (e.g., fingerprint or iris) are increasingly integrated into mobile devices to offer security advantages over traditional practices (e.g., passwords and PINs) due to their ease of use in user authentication. However, existing biometric systems are with controversy: once divulged, they are compromised forever - no one can grow a new fingerprint or iris. This work explores a truly cancelable brain-based biometric system for mobile platforms (e.g., smart headwear). Specifically, we present a new psychophysiological protocol via non-volitional brain response for trustworthy mobile authentication, with an application example of smart headwear. Particularly, we address the following research challenges in mobile biometrics with a theoretical and empirical combined manner: (1) how to generate reliable brain responses with sophisticated visual stimuli; (2) how to acquire the distinct brain response and analyze unique features in the mobile platform; (3) how to reset and change brain biometrics when the current biometric credential is divulged. To evaluate the proposed solution, we conducted a pilot study and achieved an f -score accuracy of 95.46% and equal error rate (EER) of 2.503%, thereby demonstrating the potential feasibility of neurofeedback based biometrics for smart headwear. Furthermore, we perform the cancelability study and the longitudinal study, respectively, to show the effectiveness and usability of our new proposed mobile biometric system. To the best of our knowledge, it is the first in-depth research study on truly cancelable brain biometrics for secure mobile authentication.

While malicious attacks on electronic devices (e-devices) have become commonplace, the use of e-devices themselves for malicious attacks has increased (e.g., explosives and eavesdropping). Modern e-devices (e.g., spy cameras, bugs or concealed weapons) can be sealed in parcels/boxes, hidden under clothing or disguised with cardboard to conceal their identities (named as hidden e-devices hereafter), which brings challenges in security screening. Inspection equipment (e.g., X-ray machines) is bulky and expensive. Moreover, screening reliability still rests on human performance, and the throughput in security screening of passengers and luggages is very limited. To this end, we propose to develop a low-cost and practical hidden e-device recognition technique to enable efficient screenings for threats of hidden electronic devices in daily life. First, we investigate and model the characteristics of nonlinear effects, a special passive response of electronic devices under millimeter-wave (mmWave) sensing. Based on this theory and our preliminary experiments, we design and implement, E-Eye, an end-to-end portable hidden electronics recognition system. E-Eye comprises a low-cost (i.e., under $100), portable (i.e., 11.8cm by 4.5cm by 1.8cm) and light-weight (i.e., 45.5g) 24GHz mmWave probe and a smartphone-based e-device recognizer. To validate the E-Eye performance, we conduct experiments with 46 commodity electronic devices under 39 distinct categories. Results show that E-Eye can recognize hidden electronic devices in parcels/boxes with an accuracy of more than 99% and has an equal error rate (EER) approaching 0.44% under a controlled lab setup. Moreover, we evaluate the reliability, robustness and performance variation of E-Eye under various real-world circumstances, and E-Eye can still achieve accuracy over 97%. Intensive evaluation indicates that E-Eye is a promising solution for hidden electronics recognition in daily life.

As a micro-engineered biomimetic system to replicate key functions of living organs, organ-on-a-chip (OC) technology provides a high-throughput model for investigating complex cell interactions with both high temporal and spatial resolutions in biological studies. Typically, microscopy and high-speed video cameras are used for data acquisition, which are expensive and bulky. Recently, compressed sensing (CS) has increasingly attracted attentions due to its extremely low-complexity structure and low sampling rate. However, there is no CS solution tailored for tempo-spatial information acquisition. In this paper, we propose tempo-spatial CS (TS-CS), a unified CS architecture for OC stream, which achieves significant cost reduction and truly combines sensing with compression along the temporal and spatial domains. We point out that TS-CS can consistently achieve better performance by exploiting tempo-spatial compressibility in OC data. To this end, we comprehensively evaluate the system performance by employing four different bases for CS. With comparison to the traditional way, we show that TS-CS always obtains better recovery result with a throughput bound and can achieve around throughput improvement under a reconstruction demand by applying discrete cosine transform matrix as the basis.

As the next-generation manufacturing driven force, 3D printing technology is having a transformative effect on various industrial domains and has been widely applied in a broad spectrum of applications. It also progresses towards other versatile fields with portable battery-powered 3D printers working on a limited energy budget. While reducing manufacturing energy is an essential challenge in industrial sustainability and national economics, this growing trend motivates us to explore the energy consumption of the 3D printer for the purpose of energy efficiency. To this end, we perform an in-depth analysis of energy consumption in commercial, off-the-shelf 3D printers from an instruction-level perspective. We build an instruction-level energy model and an energy profiler to analyze the energy cost during the fabrication process. From the insights obtained by the energy profiler, we propose and implement a cross-layer energy optimization solution, called 3DGates, which spans the instruction-set, the compiler and the firmware. We evaluate 3DGates over 338 benchmarks on a 3D printer and achieve an overall energy reduction of 25%.

Continuous authentication is of great importance to maintain the security level of a system throughout the login session. The goal of this work is to investigate a trustworthy, continuous, and non-contact user authentication approach based on a heart-related biometric that works in a daily-life environment. To this end, we present a novel, continuous authentication system, namely Cardiac Scan, based on geometric and non-volitional features of the cardiac motion. Cardiac motion is an automatic heart deformation caused by self-excitement of the cardiac muscle, which is unique to each user and is difficult (if not impossible) to counterfeit. Cardiac Scan features intrinsic liveness detection, unobtrusiveness, cost-effectiveness, and high usability. We prototype a remote, high-resolution cardiac motion sensing system based on the smart DC-coupled continuous-wave radar. Fiducial-based invariant identity descriptors of cardiac motion are extracted after the radar signal demodulation. We conduct a pilot study with 78 subjects to evaluate Cardiac Scan in accuracy, authentication time, permanence, evaluation in complex conditions, and vulnerability. Specifically, Cardiac Scan achieves 98.61% balanced accuracy (BAC) and 4.42% equal error rate (EER) in a real-world setup. We demonstrate that Cardiac Scan is a robust and usable continuous authentication system.

Additive manufacturing, also known as 3D printing, has been increasingly applied to fabricate highly intellectual property (IP) sensitive products. However, the related IP protection issues in 3D printers are still largely underexplored. On the other hand, smartphones are equipped with rich onboard sensors and have been applied to pervasive mobile surveillance in many applications. These facts raise one critical question: is it possible that smartphones access the side-channel signals of 3D printer and then hack the IP information? To answer this, we perform an end-to-end study on exploring smartphone-based side-channel attacks against 3D printers. Specifically, we formulate the problem of the IP side-channel attack in 3D printing. Then, we investigate the possible acoustic and magnetic side-channel attacks using the smartphone built-in sensors. Moreover, we explore a magnetic-enhanced side-channel attack model to accurately deduce the vital directional operations of 3D printer. Experimental results show that by exploiting the side-channel signals collected by smartphones, we can successfully reconstruct the physical prints and their G-code with Mean Tendency Error of 5.87% on regular designs and 9.67% on complex designs, respectively. Our study demonstrates this new and practical smartphone-based side channel attack on compromising IP information during 3D printing.

As mobile technology grows rapidly, the smartphone has become indispensable for transmitting private user data, storing the sensitive corporate files, and conducting secure payment transactions. However, with mobile security research lagging, smartphones are extremely vulnerable to unauthenticated access. In this paper, we present, EyeVeri, a novel eye-movement-based authentication system for smartphone security protection. Specifically, EyeVeri tracks human eye movement through the built-in front camera and applies the signal processing and pattern matching techniques to explore volitional and non-volitional gaze patterns for access authentication. Through a comprehensive user study, EyeVeri performs well and is a promising approach for smartphone user authentication. We also discuss the evaluation results in-depth and analyze opportunities for future work.

Sleep monitoring is receiving increased attention in the healthcare community, because the quality of sleep has a great impact on human health. Existing in-home sleep monitoring devices are either obtrusive to the user or cannot provide adequate sleep information. To this end, we present SleepSense, a contactless and low-cost sleep monitoring system for home use that can continuously detect the sleep event. Specifically, SleepSense consists of three parts: an electromagnetic probe, a robust automated radar demodulation module, and a signal processing framework for sleep event recognition, including on-bed movement, bed exit, and breathing event. We present a prototype of the SleepSense system, and perform a set of comprehensive experiments to evaluate the performance of sleep monitoring. Using a real-case evaluation, experimental results indicate that SleepSense can perform effective sleep event detection and recognition in practice.

Wireless sensor nodes advance the brain-computer interface (BCI) from laboratory setup to practical applications. Compressed sensing (CS) theory provides a sub-Nyquist sampling paradigm to improve the energy efficiency of electroencephalography (EEG) signal acquisition. However, EEG is a structure-variational signal with time-varying sparsity, which decreases the efficiency of compressed sensing. In this paper, we present a new adaptive CS architecture to tackle the challenge of EEG signal acquisition. Specifically, we design a dynamic knob framework to respond to EEG signal dynamics, and then formulate its design optimization into a dynamic programming problem. We verify our proposed adaptive CS architecture on a publicly available data set. Experimental results show that our adaptive CS can improve signal reconstruction quality by more than 70% under different energy budgets while only consuming 187.88 nJ/event. This indicates that the adaptive CS architecture can effectively adapt to the EEG signal dynamics in the BCI.