1. Develop affective brain-computer interface infrastructure (link, 2019.10 updated).

2. Exploit EEG-markers for cognitive assessment (link, 2021.7 updated).

3. Develop wearable LEGO-like EEG headset and its hyperscanning scenario  (link, 2022.3 updated). 

Intra- and inter-individual non-stationary EEG oscillations of emotional responses

Abstract. Electroencephalogram (EEG)-based affective brain-computer interfaces (aBCIs) have been attracting ever-growing interest and research resources. Whereas most previous neuroscience studies have focused on single-day/-session recording and sensor-level analysis, less effort has been invested in assessing the fundamental nature of non-stationary EEG oscillations underlying emotional responses across days and individuals. This work thus aimed to use a data-driven blind source separation method, i.e., independent component analysis (ICA), to derive emotion-relevant spatio-spectral EEG source oscillations and assess the extent of non-stationarity. To this end, this work conducted an 8-day music-listening experiment (i.e., roughly interspaced over 2 months) and recorded whole-scalp 30-ch EEG data from 10 subjects. Given the large size of the data (i.e., from 80 sessions), results indicated that EEG non-stationarity was clearly revealed in the numbers and locations of brain sources of interest as well as their spectral modulation to the emotional responses. Less than half of subjects (two to four) showed the same relatively day-stationary (source reproducibility >6 days) spatio-spectral tendency towards one of the binary valence and arousal states. This work substantially advances the previous work by exploiting intra- and inter-individual EEG variability in an ecological multiday scenario. Such EEG non-stationarity may inevitably present a great challenge for the development of an accurate, robust, and generalized emotion-classification model.  

Yi-Wei Shen and Yuan-Pin Lin*, "Challenge for Affective Brain-Computer Interfaces: Non-stationary Spatio-spectral EEG Oscillations of Emotional Responses, " Frontiers in Human Neuroscience, 13:366, 2019.

A personalized cross-day model using within- and cross-dataset transfer learning

Abstract. State-of-the-art electroencephalogram (EEG)-based emotion-classification works indicate that a personalized model may not be well exploited until sufficient labeled data are available, given a substantial EEG non-stationarity over days. However, it is impractical to impose a labor-intensive, time-consuming multiple-day data collection. This study proposes a robust principal component analysis (RPCA)-embedded transfer learning (TL) to generate a personalized cross-day model with less labeled data, while obviating intra- and inter-individual differences. Upon the add-session-in validation on two datasets MDME (five-day data of 12 subjects) and SDMN (single-day data of 26 subjects), the experimental results showed that TL enabled the classifier of an MDME individual (using his/her 1st-day session only) to improve progressively in valence and arousal classification by adding similar source sessions (SSs) via the within-dataset TL (wdTL) and cross-dataset TL (cdTL) manners. When recruiting three SSs to test on the 5th-day session, the wdTL improvement (valence: 11.19%, arousal: 5.82%) marginally outperformed the subject-dependent (SD) counterpart (valence: 9.75%, arousal: 3.77%) that was obtained using their own 2nd-4th-day sessions only. The cdTL returned a similar trend in valence (8.35%), yet it was less effective in arousal (0.81%). Most importantly, such cross-day enhancements did not occur in either SD or TL scenarios until RPCA processing. This work sheds light on how to construct a personalized model by leveraging ever-growing EEG repositories. 

Yuan-Pin Lin*, "Constructing a Personalized Cross-day EEG-based Emotion-Classification Model Using Transfer Learning, " IEEE Journal of Biomedical and Health Informatics, 2019 (early access).

Alleviating cross-day EEG non-stationarity using robust principal component analysis

Abstract. Constructing a robust emotion-aware analytical framework using non-invasively recorded electroencephalogram (EEG) signals has gained intensive attentions nowadays. However, as deploying a laboratory-oriented proof-of-concept study toward real-world applications, researchers are now facing an ecological challenge that the EEG patterns recorded in real life substantially change across days (i.e., day-to-day variability), arguably making the pre-defined predictive model vulnerable to the given EEG signals of a separate day. The present work addressed how to mitigate the inter-day EEG variability of emotional responses with an attempt to facilitate cross-day emotion classification, which was less concerned in the literature. This study proposed a robust principal component analysis (RPCA)-based signal filtering strategy and validated its neurophysiological validity and machine-learning practicability on a binary emotion classification task (happiness vs. sadness) using a five-day EEG dataset of 12 subjects when participated in a music-listening task. The empirical results showed that the RPCA-decomposed sparse signals (RPCA-S) enabled filtering off the background EEG activity that contributed more to the inter-day variability, and predominately captured the EEG oscillations of emotional responses that behaved relatively consistent along days. Through applying a realistic add-day-in classification validation scheme, the RPCA-S progressively exploited more informative features (from 12.67 ± 5.99 to 20.83 ± 7.18) and improved the cross-day trained the EEG signals from one to four recording days and tested against one unseen binary emotion-classification accuracy (from 58.31 ± 12.33% to 64.03 ± 8.40%) as subsequent day. The original EEG features (prior to RPCA processing) neither achieved the cross-day classification (the accuracy was around chance level) nor replicated the encouraging improvement due to the inter-day EEG variability. This result demonstrated the effectiveness of the proposed method and may shed some light on developing a realistic emotion-classification analytical framework alleviating day-to-day variability.

Yuan-Pin Lin*, Ping-Keng Jao, and Yi-Hsuan Yang, "Improving Cross-day EEG-based Emotion Classification Using Robust Principal Component Analysis, " Frontiers in Computational Neuroscience, 11:64, 2017.

Improving a personalized model using cross-subject transfer learning

Abstract. To overcome the individual differences, an accurate electroencephalogram (EEG)-based emotion-classification system requires a considerable amount of ecological calibration data for each individual, which is labor-intensive and time-consuming. Transfer learning (TL) has drawn increasing attention in the field of EEG signal mining in recent years. The TL leverages existing data collected from other people to build a model for a new individual with little calibration data. However, brute-force transfer to an individual (i.e., blindly leveraged the labeled data from others) may lead to a negative transfer that degrades performance rather than improving it. This study thus proposed a conditional TL (cTL) framework to facilitate a positive transfer (improving subject- specific performance without increasing the labeled data) for each individual. The cTL first assesses an individual’s transferability for positive transfer and then selectively leverages the data from others with comparable feature spaces. The empirical results showed that among 26 individuals, the proposed cTL framework identified 16 and 14 transferable individuals who could benefit from the data from others for emotion valence and arousal classification, respectively. These transferable individuals could then leverage the data from 18 and 12 individuals who had similar EEG signatures to attain maximal TL improvements in valence- and arousal-classification accuracy. The cTL improved the overall classification performance of 26 individuals by ∼15% for valence categorization and ∼12% for arousal counterpart, as compared to their default performance based solely on the subject-specific data. This study evidently demonstrated the feasibility of the proposed cTL framework for improving an individual’s default emotion-classification performance given a data repository. The cTL framework may shed light on the development of a robust emotion-classification model using fewer labeled subject-specific data toward a real-life affective brain-computer interface (ABCI).

Yuan-Pin Lin* and Tzyy-Ping Jung, "Improving EEG-based Emotion Classification Using Conditional Transfer Learning, " Frontiers in Human Neuroscience, 11:334, 2017.

Objective Assessment of Impulse Control Disorder in Patients with Parkinson’s Disease

(This is a collaborative project with Kaohsiung Chang Gung Memorial Hospital)

Abstract. Patients with Parkinson’s disease (PD) can develop impulse control disorders (ICDs) while undergoing a pharmacological treatment for motor control dysfunctions with a dopamine agonist (DA). Conventional clinical interviews or questionnaires can be biased and may not accurately diagnose at the early stage. A wearable electroencephalogram (EEG)-sensing headset paired with an examination procedure can be a potential user-friendly method to explore ICD-related signatures that can detect its early signs and progression by reflecting brain activity. A stereotypical Go/NoGo test that targets impulse inhibition was performed on 59 individuals, including healthy controls, patients with PD, and patients with PD diagnosed by ICDs. We conducted two Go/NoGo sessions before and after the DA-pharmacological treatment for the PD and ICD groups. A low-cost LEGO-like EEG headset was used to record concurrent EEG signals. Then, we used the event-related potential (ERP) analytical framework to explore ICD-related EEG abnormalities after DA treatment. After the DA treatment, only the ICD-diagnosed PD patients made more behavioral errors and tended to exhibit the deterioration for the NoGo N2 and P3 peak amplitudes at fronto-central electrodes in contrast to the HC and PD groups. Particularly, the extent of the diminished NoGo-N2 amplitude was prone to be modulated by the ICD scores at Fz with marginal statistical significance (r=-0.34, p=0.07). The low-cost LEGO-like EEG headset successfully captured ERP waveforms and objectively assessed ICD in patients with PD undergoing DA treatment. This objective neuro-evidence could provide complementary information to conventional clinical scales used to diagnose ICD adverse effects.

Yuan-Pin Lin, Hsing-Yi Liang, Yueh-Sheng Chen, Cheng-Hsien Lu, Yih-Ru Wu, Yung-Yee Chang, and Wei-Che Lin, "Objective Assessment of Impulse Control Disorder in Patients with Parkinson’s Disease Using a Low-Cost LEGO-like EEG Headset: A Feasibility Study, " Journal of NeuroEngineering and Rehabilitation, 18:109, 2021.

Implementing a portable 8-ch EEG-acquisition hardware 

Abstract. Multi-subject electroencephalogram (EEG) computing has attracted increasing interest in recent years. This work developed a cost-efficient, portable, and customizable system for a simultaneous EEG recording from multiple subjects. The developed system implemented two core hardware infrastructures, including an event broadcaster and a dry electrode-compatible EEG amplifier by assembling entirely low-cost, off-the-shelf electronic components. The broadcaster allowed distribution of event markers to multiple EEG amplifiers concurrently, whereas the amplifier transmitted the digitized, event- synchronized EEG signals wirelessly. By conducting an oddball event-related potential (ERP) experiment with simultaneous recordings of three subjects on 10 days, the system reliably captured the time- and phase-locked ERP components (e.g., N100 and P300 amplitudes) by single-subject, multi-subject, and multi-day analytical approaches. The practicality and stability of the proposed system was empirically demonstrated in terms of the signal quality, EEG-event synchronization, and inter-amplifier coordination for a multi-subject setup. 

Kai-Chiang Chuang and Yuan-Pin Lin*, "Cost-Efficient, Portable, and Custom Multi-Subject Electroencephalogram Recording System, " IEEE Access, vol. 7, pp. 56760-56769, 2019.

LEGO-like EEG sensor-holder assembly infrastructure 

Abstract. Mobile electroencephalogram (EEG)-sensing technologies have rapidly progressed and made the access of neuroelectrical brain activity outside the laboratory in everyday life more realistic. However, most existing EEG headsets exhibit a fixed design, whereby its immobile montage in terms of electrode density and coverage inevitably poses a great challenge with applicability and generalizability to the fundamental study and application of the brain-computer interface (BCI). In this study, a cost-efficient, custom EEG-electrode holder infrastructure was designed through the assembly of primary components, including the sensor-positioning ring, inter-ring bridge, and bridge shield. It allows a user to (re)assemble a compact holder grid to accommodate a desired number of electrodes only to the regions of interest of the brain and iteratively adapt it to a given head size for optimal electrode-scalp contact and signal quality. This study empirically demonstrated its easy-to-fabricate nature by a low-end fused deposition modeling (FDM) 3D printer and proved its practicability of capturing event-related potential (ERP) and steady-state visual-evoked potential (SSVEP) signatures over 15 subjects. This paper highlights the possibilities for a cost-efficient electrode-holder assembly infrastructure with replaceable montage, flexibly retrofitted in an unlimited fashion, for an individual for distinctive fundamental EEG studies and BCI applications.

Yuan-Pin Lin*, Ting-Yu Chen, and Wei-Jen Chen, "Cost-efficient and Custom Electrode-holder Assembly Infrastructure for EEG Recordings, " Sensors, vol. 19, pp. 4273, 2019. 

Yuan-Pin Lin, "Assembled Holder Structure for Positioning Brain Activity Sensors", Taiwan Patent Application, M568690, 2018.

SSVEP-based BCI Shooting Contest Using a Cost-efficient EEG Hyperscanning Platform

A Simultaneous SSVEP Decoding of Four Subjects Using a Cost-efficient EEG Hyperscanning Platform

An SSVEP-basis 4-subject Brain-Computer Music Interface (BCMI) Demonstration

This BCMI system allow four users to virtually play one of the four instruments (e.g., piano, bass, drums, and organ) and control their predefined settings in either energetic (sun), calm (moon), moody (rain), or mute (no sound) mode for a co-play of musical accompaniment. This demonstation is one of practical outcomes for an international collaborative project with Jāzeps Vītols Latvian Academy of Music, Latvia (PI: Dr. Valdis Bernhofs) and Vilnius University, Lithuania (PI: Dr. Inga Griškova-Bulanova), which was suported by the Ministry of Science and Technology, Taiwan (PI: Dr. Yuan-Pin Lin), under the grant MOST 108-2923-E-110-001-MY3.

Acknowledgment: Hung-Yuan Huang (Taiwan) for SSVEP hyperscanning platform and Jachin Pousson (Latvia) for music engine design and protocol.

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