Portable electroencephalography in early detection of depression: Progress and future directions

Table 1 Study quality assessment overview

Ref.	Bias risk assessment	Sample size assessment	Validation method assessment	Evidence level (OCEBM)	Overall quality score
Khan et al[44]	Low	Reasonable	Unreasonable	5	Medium
Wu et al[34]	Unclear	Unreasonable	Unreasonable	3	Low
Tian et al[45]	Low	Unreasonable	Reasonable	2	Medium
Tian et al[38]	Low	Reasonable	Reasonable	4	High
Sakib et al[43]	Unclear	Reasonable	Reasonable	5	Medium
Sharma et al[41]	Medium	Unreasonable	Unreasonable	4	Low
Lei et al[39]	Medium	Unreasonable	Unreasonable	4	Low
Morita et al[40]	High	Reasonable	Unreasonable	5	Low
Wang et al[46]	Medium	Unreasonable	Reasonable	5	Medium
Saleem et al[42]	Unclear	Reasonable	Unreasonable	5	Medium

OCEBM: Oxford Centre for Evidence-based Medicine.

Table 2 Categories of portable electroencephalography

Type	Technical characteristics	Advantages	Limitations	Ref.
Head-worn EEG	Dry/wet electrodes, rigid/semi-flexible headset, 4-16 channels	Detects depression-related EEG biomarkers in key prefrontal regions, suitable for lab or remote screening	Limited comfort, prone to motion artifacts	[17-19,23,24,26,39]
Ear-worn EEG	Sensors in earplugs or ear-mounted devices, dry electrodes, 1-2 channels	All-day wearability, suitable for daily emotional monitoring, supports auditory stimulus experiments	Limited signal coverage, susceptible to EMG noise	[55-57]
Textile-integrated EEG	Sensors embedded in wearable textiles (e.g., smart caps, headbands), conductive materials	Suitable for long-term monitoring, natural user experience, home or clinical use	Lower signal quality than standard EEG, affected by textile movement	[20,58]
Implantable EEG	High-precision EEG acquisition, ultra-thin flexible electrodes for skin adhesion	Precise monitoring for severe depression, can be combined with tDCS for therapy, high-quality data	Highly invasive, limited to specific medical contexts, patch requires frequent replacement	[59]

EEG: Electroencephalography; EMG: Electromyogram; tDCS: Transcranial direct current stimulation.

Table 3 Portable electroencephalography in depression detection

Feature type	Models	Key indicators	Main results	Ref.
Frequency-domain (PSD)	Support vector machine, random forest, CNN, RNN; feature combination	Decreased frontal α power; increased β power; abnormal θ/δ power	Frontal EEG + PSD: 90.7% accuracy; combined with nonlinear features improves stability	[34,38,39,43]
Time-domain	LSTM, feature fusion (time + frequency), classifiers	Amplitude, peak, mean value	Used for signal evaluation; combination improves accuracy	[34,41,44,45]
Time-frequency (STFT/WT)	STFT, WT + nonlinear features, classifiers	α/β power; WT adapts to time scales	WT extracts nonlinear features; high accuracy; significant power asymmetry	[38,40,42,45]
Nonlinear features	Nonlinear features + classifiers	SampEn, ApEn, FD, HFD, LZC	Lower complexity in depression; LZC and HFD achieve approximately 90% accuracy	[40,43]
Functional connectivity	Coherence, PLV + classifiers	Decreased frontal coherence, PLV; reduced frequency synchronization	Reduced interhemispheric connectivity; linked to emotion regulation	[34,38]

PSD: Power spectral density; CNN: Convolutional neural network; RNN: Recurrent neural network; EEG: Electroencephalography; LSTM: Long short-term memory; STFT: Short-time Fourier transform; WT: Wavelet transform; SampEn: Sample entropy; ApEn: Approximate entropy; FD: Fractal dimension; HFD: Higuchi fractal dimension; LZC: Lempel-Ziv complexity; PLV: Phase locking value.